Spatio-temporal variation of sound speed,in seafloor geodetic precise positioning,can always be attributed to the time error.Firstly,this paper analyzes the existing error compensation model,i.e.,the time ratio model,...Spatio-temporal variation of sound speed,in seafloor geodetic precise positioning,can always be attributed to the time error.Firstly,this paper analyzes the existing error compensation model,i.e.,the time ratio model,which is expressed by the recorded time multiplying a ratio coefficient.And then a time split model is proposed by expressing the acoustic ray traveling time as the recorded time pluses a perturbation time error.The theoretical differences between the proposed time bias compensation model and the time ratio model are analyzed.Under the new framework,sound speed perturbation models with optimal single-layer spatial gradient and multi-layer spatial gradients are developed to compensate for sound speed error in the complex cases.Numerical computation shows that the simple time split model keeps the same accuracy as some complicated models while considering the distribution of random error.Furthermore,multi-layer model can improve the positioning accuracy without putting the pressure on parametrization.展开更多
The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic...The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic(GNSS-A) positioning technology typically uses in-situ sound speed profiles(SSPs) and considers the impact of these variations at the data post-processing stage. However, in-situ SSP measurement is costly and somewhat hinders the timeliness of seafloor geodetic monitoring. We generalize the bilinear SSP(BL-SSP) to be a piecewise-linear SSP, whose model parameters are estimated from GNSS-A observations. In addition, we construct a set of constraints based on a priori marine environment observation to stabilize SSP inversion and propose an algorithm to recursively conduct the inversion, e.g.,the trilinear SSP(TL-SSP) inversion is initialized using the BL-SSP inversion result. The proposed model is verified by long-term GNSS-A seafloor geodetic observations. It shows that the root mean square error(RMSE) of the TL-SSP inversion result is 10.87 m/s, compared to 11.08 m/s for the traditional BL-SSP, with significant improvements observed in shallow and middle water layers. Furthermore, when replacing the in-situ SSP with the inverted SSP for precise seafloor geodetic positioning and incorporating the acoustic delay parameters, the TL-SSP-based positioning demonstrates higher accuracy than the BL-SSP-based approach. Relative to the positioning result based on the in-situ SSP, the mean bias, standard deviation and RMSE of the horizontal positioning error are better than 0.003 m, 0.005 m,and 0.006 m, respectively, while those of the vertical positioning error are better than 0.03 m, 0.04 m, and 0.04 m,respectively. Compared with BL-SSP, TL-SSP can achieve a positioning error reduction along the E-direction, Ndirection, and U-direction by 16.7%, 15.0%, and 5.5%, respectively.展开更多
Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnos...Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.展开更多
A sound speed profile plays an important role in shallow water sound propagation.Concurrent with in-situ measurements,many inversion methods,such as matched-field inversion,have been put forward to invert the sound sp...A sound speed profile plays an important role in shallow water sound propagation.Concurrent with in-situ measurements,many inversion methods,such as matched-field inversion,have been put forward to invert the sound speed profile from acoustic signals.However,the time cost of matched-field inversion may be very high in replica field calculations.We studied the feasibility and robustness of an acoustic tomography scheme with matched-field processing in shallow water,and described the sound speed profile by empirical orthogonal functions.We analyzed the acoustic signals from a vertical line array in ASIAEX2001 in the East China Sea to invert sound speed profiles with estimated empirical orthogonal functions and a parallel genetic algorithm to speed up the inversion.The results show that the inverted sound speed profiles are in good agreement with conductivity-temperature-depth measurements.Moreover,a posteriori probability analysis is carried out to verify the inversion results.展开更多
Building empirical equations is an effective way to link the acoustic and physical properties of sediments.These equations play an important role in the prediction of sediments sound speeds required in underwater acou...Building empirical equations is an effective way to link the acoustic and physical properties of sediments.These equations play an important role in the prediction of sediments sound speeds required in underwater acoustics.Although many empirical equations coupling acoustic and physical properties have been developed over the past few decades,further confirmation of their applicability by obtaining large amounts of data,especially for equations based on in situ acoustic measurement techniques,is required.A sediment acoustic survey in the South Yellow Sea from 2009 to 2010 revealed statistical relationships between the in situ sound speed and sediment physical properties.To improve the comparability of these relationships with existing empirical equations,the present study calculated the ratio of the in situ sediment sound speed to the bottom seawater sound speed,and established the relationships between the sound speed ratio and the mean grain size,density and porosity of the sediment.The sound speed of seawater at in situ measurement stations was calculated using a perennially averaged seawater sound speed map by an interpolation method.Moreover,empirical relations between the index of impedance and the sound speed and the physical properties were established.The results confirmed that the existing empirical equations between the in situ sound speed ratio and the density and porosity have general suitability for application.This study also considered that a multiple-parameter equation coupling the sound speed ratio to both the porosity and the mean grain size may be more useful for predicting the sound speed than an equation coupling the sound speed ratio to the mean grain size.展开更多
At present,GNSS-Acoustic(GNSS-A)combined technology is widely used in positioning for seafloor geodetic stations.Based on Sound Velocity Profiles(SVPs)data,the equal gradient acoustic ray-tracing method is applied in ...At present,GNSS-Acoustic(GNSS-A)combined technology is widely used in positioning for seafloor geodetic stations.Based on Sound Velocity Profiles(SVPs)data,the equal gradient acoustic ray-tracing method is applied in high-precision position inversion.However,because of the discreteness of the SVPs used in the forementioned method,it ignores the continuous variation of sound velocity structure in time domain,which worsens the positioning accuracy.In this study,the time-domain variation of Sound Speed Structure(SSS)has been considered,and the cubic B-spline function is applied to characterize the perturbed sound velocity.Based on the ray-tracing theory,an inversion model of“stepwise iteration&progressive corrections”for both positioning and sound speed information is proposed,which conducts the gradual correction of seafloor geodetic station coordinates and disturbed sound velocity.The practical data was used to test the effectiveness of our method.The results show that the Root Mean Square(RMS)errors of the residual values of the traditional methods without sound velocity correction,based on quadratic polynomial correction and based on cubic B-spline function correction are 1.43 ms,0.44 ms and 0.21 ms,respectively.The inversion model with sound velocity correction can effectively eliminate the systematic error caused by the change of SSS,and significantly improve the positioning accuracy of the seafloor geodetic stations.展开更多
Traditional acquisition method of sound speed profiles using hydro-acoustic instruments is accurate but time-consuming and costly.To overcome this problem,some inversion methods have been developed over the last few d...Traditional acquisition method of sound speed profiles using hydro-acoustic instruments is accurate but time-consuming and costly.To overcome this problem,some inversion methods have been developed over the last few decades.In this study,a comprehensive comparison of two inversion methods–the acoustic inversion method(AIM)and the satellite observation reconstruction method(SOR)–is presented.For AIM,the sound speed profile is first parameterized by the empirical orthogonal function(EOF)and the optimal parameters are searched by simulated annealing algorithm with respect to the cross-correlation function of the receiving signal and the simulation signal.For SOR,remotely sensed data are used to construct sound speed profiles.An experiment was conducted in the northeast of the South China Sea to verify the two methods.Both methods can obtain sound speed profiles quickly and cheaply.Compared with the sound speed profiles obtained by a conductivity-temperature-depth(CTD)instrument,the root-meansquare-error(RMSE)of AIM is 0.55 m s^(−1) and that of SOR is 1.71 m s^(−1).It is clear that AIM provides better inversion performance than SOR.Another primary benefit of AIM is that this method has no limitation to the inversion depth.The simulation results of sound propagation in regard to the inversed sound speed profiles show that the transmission losses of AIM and CTD are consistent and that of SOR is adversely affected by the inversion error of the sound speed and the inversion depth.But SOR has particular advantages in the inversion coverage.Together,all of these advantages make the AIM particularly valuable in practice.展开更多
With the consumption of terrestrial metal resources,the exploitation of deep-sea polymetallic nodule minerals has been widely concerned around the world.Therefore,the environmental impact of deep-sea polymetallic nodu...With the consumption of terrestrial metal resources,the exploitation of deep-sea polymetallic nodule minerals has been widely concerned around the world.Therefore,the environmental impact of deep-sea polymetallic nodule mining cannot be ignored.However,duo to the lacks in stable and safe deep-sea(the depth>1000 m)vertical profile observation systems and consequently in long-term in-situ observation data,the sound speed and dissolved oxygen and the other water environment factors in the deposition areas of polymetallic nodules remains poorly understood.In this study,a deep-sea in-situ observation system was designed and deployed,and the water environment data of the polymetallic nodule deposition area were collected and analyzed.Result shows that the dissolved oxygen in the depth of 0–600 m was mainly affected by biological factors,while that in the area deeper than 600 m was affected by physical factors.The sound speed in the water body was mainly affected by temperature and pressure.At depths below 840 m,the sound speed is mainly controlled by temperature,and at depths between 840 m and 5700 m,the sound speed is mainly controlled by pressure.The correlations of sound speed vs.pressure and vs.temperature were regressed into equation.The resuspension of sediments rich in various metals may result in the reduction of dissolved oxygen and the improvement of redox potential.This environmental impact caused by a single sediment resuspension could last for 24 h or more.These findings enrich the understanding of the background value of the water environment in the polymetallic nodule deposition area.展开更多
It is essential to ac quire sound speed profiles(SSPs)in high-precision spatiotemporal resolution for undersea acoustic activities.However,conventional observation methods cannot obtain high-resolution SSPs.Besides,S ...It is essential to ac quire sound speed profiles(SSPs)in high-precision spatiotemporal resolution for undersea acoustic activities.However,conventional observation methods cannot obtain high-resolution SSPs.Besides,S SPs are complex and changeable in time and space,especially in coastal areas.We proposed a new space-time multigrid three-dimensional variational method with weak constraint term(referred to as STC-MG3DVar)to construct high-precision spatiotemporal resolution SSPs in coastal areas,in which sound velocity is defined as the analytical variable,and the Chen-Millero sound velocity empirical formula is introduced as a weak constraint term into the cost function of the STC-MG3DVar.The spatiotemporal correlation of sound velocity observations is taken into account in the STC-MG3DVar method,and the multi-scale information of sound velocity observations from long waves to short waves can be successively extracted.The weak constraint term can optimize sound velocity by the physical relationship between sound velocity and temperature-salinity to obtain more reasonable and accurate SSPs.To verify the accuracy of the STC-MG3DVar,SSPs observations and CTD observations(temperature observations,salinity observations)are obtained from field experiments in the northern coastal area of the Shandong Peninsula.The average root mean square error(RMSE)of the STC-MG3DVar-constructed SSPs is 0.132 m/s,and the STC-MG3DVar method can improve the SSPs construction accuracy over the space-time multigrid 3DVar without weak constraint term(ST-MG3DVar)by 10.14%and over the spatial multigrid 3DVar with weak constraint term(SC-MG3DVar)by 44.19%.With the advantage of the constraint term and the spatiotemporal correlation information,the proposed STC-MG3DVar method works better than the ST-MG3DVar and the SCMG3DVar in constructing high-precision spatiotemporal re solution SSPs.展开更多
There are numerous formulae relating to the predictions of sound wave in the cavitating and bubbly flows. However, tile valid regions of those formulae are rather unclear from the view point of physics. In this work, ...There are numerous formulae relating to the predictions of sound wave in the cavitating and bubbly flows. However, tile valid regions of those formulae are rather unclear from the view point of physics. In this work, the validity of the existing formulae is discussed in terms of three regions by employing the analysis of three typical lengths involved （viscous length, thermal diffusion length and bubble radius）. In our discussions, viscosity and thermal diffusion are both considered together with the effects of relative motion between bubbles and liquids. The importance of relative motion and thermal diffusion are quantitatively discussed in a wide range of parameter zones （including bubble radius and acoustic frequency）, The results show that for large bubbles, the effects of relative motion will be prominent in a wide region.展开更多
Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment(REA)in the South China Sea(SCS).In this paper,the remote sensing data were used to estimate s...Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment(REA)in the South China Sea(SCS).In this paper,the remote sensing data were used to estimate sound speed profile(SSP)with the self-organizing map(SOM)method in the SCS.First,the consistency of the empirical orthogonal functions was examined by using k-means clustering.The clustering results indicated that SSPs in the SCS have a similar perturbation nature,which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability.Second,a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy.Validation revealed promising results,with a mean reconstruction error of 1.26 m/s,which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression(sEOF-r)method.By violating the constraints of linear inversion,the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation.The improvements to enhance the accuracy and robustness of REA in the SCS were offered.These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data.展开更多
The estimation of ocean sound speed profiles(SSPs)requires the inversion of an acoustic field using limited observations.Such inverse problems are underdetermined,and require regularization to ensure physically realis...The estimation of ocean sound speed profiles(SSPs)requires the inversion of an acoustic field using limited observations.Such inverse problems are underdetermined,and require regularization to ensure physically realistic solutions.The empirical orthonormal function(EOF)is capable of a very large compression of the data set.In this paper,the non-linear response of the sound pressure to SSP is linearized using a first order Taylor expansion,and the pressure is expanded in a sparse domain using EOFs.Since the parameters of the inverse model are sparse,compressive sensing(CS)can help solve such underdetermined problems accurately,efficiently,and with enhanced resolution.Here,the orthogonal matching pursuit(OMP)is used to estimate range-independent acoustic SSPs using the simulated acoustic field.The superior resolution of OMP is demonstrated with the SSP data from the South China Sea experiment.By shortening the duration of the training set,the temporal correlation between EOF and test sets is enhanced,and the accuracy of sound velocity inversion is improved.The SSP estimation error versus depth is calculated,and the 99%confidence interval of error is within±0.6 m/s.The 82%of mean absolute error(MAE)is less than 1 m/s.It is shown that SSPs can be well estimated using OMP.展开更多
Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, ...Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, thesea surface data could be obtained in near-real time. Typically, the subsurface fields are correlated with the sea surfaceparameters. Thus, the SSPs could be obtained by means of satellite remote sensing. In this paper, the history as wellas the current research over the reconstruction of subsurface fields by means of sea surface data is introduced. Thentwo methods to reconstruct the SSPs with sea surface data, including the linear regression method using the empiricalorthogonal function, and the self-organizing method based on the big data theory, are described in detail in the paper.展开更多
Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array tr...Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. Ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.展开更多
The Global Navigation Satellite System–Acoustic(GNSS-A)combined positioning technique extends geodetic networks into the seafoor.Currently,GNSS-A can achieve static seafoor positioning accuracy at centimeter level.Ho...The Global Navigation Satellite System–Acoustic(GNSS-A)combined positioning technique extends geodetic networks into the seafoor.Currently,GNSS-A can achieve static seafoor positioning accuracy at centimeter level.However,in practical operations,substantial time,manpower,fnancial and instrument resources are required to measure in situ Sound Speed Profles(SSPs).This paper evaluates the feasibility of GNSS-A with alternative SSPs instead of in situ measurements.The GNSS-A positioning using three diferent SSPs are compared:the Munk empirical profle,the profles from the HYbrid Coordinate Ocean Model(HYCOM)global ocean analysis product,and the in situ profles.Compared with the in situ profle,the Munk SSP has little impact on the GNSS-A horizontal position(0.6 cm in root-mean-square,RMS)but introduces a large systematic error in the vertical position(10.3 cm in RMS),and the impact on the displacement velocity is at the mm/a level.When the HYCOM profle is substituted for in situ profles,the impact on GNSS-A positioning is only 0.2 cm in the horizontal and 2.9 cm in the vertical,and the impact on displacement velocity is at the sub-mm/a level in the horizontal and mm/a level in the vertical.The HYCOM global ocean analysis SSPs can largely serve as a cost-efective substitute for in situ profles in GNSS-A seafoor positioning,which is especially applicable to GNSS-A measurements using unmanned surface vehicles,for which full-depth SSP measurements are difcult.Therefore,when SSPs are selected,appropriate decisions should be made on the basis of specifc GNSS-A application needs and conditions.展开更多
Autonomous and Remotely-operated Vehicles(ARVs)rely on precise underwater navigation via integrated Ultra-Short Baseline(USBL)acoustic positioning system and Strap-down Inertial Navigation System(SINS).However,spatiot...Autonomous and Remotely-operated Vehicles(ARVs)rely on precise underwater navigation via integrated Ultra-Short Baseline(USBL)acoustic positioning system and Strap-down Inertial Navigation System(SINS).However,spatiotemporal variations in underwater Sound Speed Profle(SSP)degrade USBL performance,reducing overall navigation accuracy.This study proposes a novel in-situ SSP correction scheme for SINS/USBL integration.We analyze SSP temporal variation with the USBL positioning scheme to build a Two Dimensional(2D)temporal SSP model;then derive partial derivatives(based on equal-gradient ray-tracing)to quantify the displacements from azimuth,incident angle,and propagation time errors;and fnally develop an adaptive two-stage information flter to estimate sound speed perturbation and detect USBL outliers.Simulations and South China Sea trials are conducted to verify its efectiveness.Compared with the traditional tight-coupling method,root mean square errors are reduced from 0.45m and 0.23 m with the traditional tightly-coupled method to 0.08 m and 0.07 m with the in-situ SSP correction scheme,representing improvements of 82.2%in the north and 69.6%in the east directions,respectively.Experimental results demonstrate that the proposed method efectively estimates the sound speed disturbance in real time,thereby signifcantly improving the performance of tightly integrated inertial-acoustic navigation systems.展开更多
To address the problem of underwater sound speed profile(SSP)inversion in underwater acoustic multipath channels,this paper combines deep learning and ray theory to propose an inversion method using a long short-term ...To address the problem of underwater sound speed profile(SSP)inversion in underwater acoustic multipath channels,this paper combines deep learning and ray theory to propose an inversion method using a long short-term memory(LSTM)network.Based on the equidistant characteristics of the horizontal line array,the proposed method takes the sensing matrix composed of multi-modal data,such as time difference of arrival and angle of arrival,as input,and utilizes the ability of the LSTM network to process timeseries data to mine the correlations between spatially ordered receiving array elements for sound speed profile inversion.On this basis,a time delay estimation method based on hard threshold estimation method and cross-correlation function is proposed to reduce the measurement errors of the sensing matrix and improve the anti-multipath performance.The feasibility and accuracy of the proposed method are verified through numerical simulations.Compared with the traditional optimization algorithm,the proposed algorithm better captures the nonlinear characteristics of SSP,with higher inversion accuracy and stronger noise resistance.展开更多
In-field Sound Speed Profile(SSP)measurement is still indispensable for achieving centimeter-level-precision Global Navigation Satellite System(GNSS)-Acoustic(GNSS-A)positioning in current state of the art.However,in-...In-field Sound Speed Profile(SSP)measurement is still indispensable for achieving centimeter-level-precision Global Navigation Satellite System(GNSS)-Acoustic(GNSS-A)positioning in current state of the art.However,in-field SSP measurement on the one hand causes a huge cost and on the other hand prevents GNSS-A from global seafloor geodesy especially for real-time applications.We propose an Empirical Sound Speed Profile(ESSP)model with three unknown temperature parameters jointly estimated with the seafloor geodetic station coordinates,which is called the 1st-level optimization.Furthermore,regarding the sound speed variations of ESSP we propose a so-called 2nd-level optimization to achieve the centimeter-level-precision positioning for monitoring the seafloor tectonic movement.Long-term seafloor geodetic data analysis shows that,the proposed two-level optimization approach can achieve almost the same positioning result with that based on the in-field SSP.The influence of substituting the in-field SSP with ESSP on the horizontal coordinates is less than 3 mm,while that on the vertical coordinate is only 2–3 cm in the standard deviation sense.展开更多
An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equ...An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.展开更多
In the underwater medium,the speed of sound varies with water depth,temperature,and salinity.The inhomogeneity of water leads to bending of sound rays,making the existing localization algorithms based on straightline ...In the underwater medium,the speed of sound varies with water depth,temperature,and salinity.The inhomogeneity of water leads to bending of sound rays,making the existing localization algorithms based on straightline propagation less precise.To realize high-precision node positioning in underwater acoustic sensor networks(UASNs),a multi-layer isogradient sound speed profile(SSP)model is developed using the linear segmentation approximation approach.Then,the sound ray tracking problem is converted into a polynomial root-searching problem.Based on the derived gradient of the signal’s Doppler shift at the sensor node,a novel underwater node localization algorithm is proposed using both the time difference of arrival(TDOA)and frequency difference of arrival(FDOA).Simulations are implemented to illustrate the effectiveness of the proposed algorithm.Compared with the traditional straight-line propagation method,the proposed algorithm can effectively handle the sound ray bending phenomenon.Estimation accuracy with different SSP modeling errors is also investigated.Overall,accurate and reliable node localization can be achieved.展开更多
基金The National Natural Science Foundation of China under contract No.41931076the National Center for Basic Sciences Project under contract No.42388102the Laoshan Laboratory under contract No.LSKJ202205100.
文摘Spatio-temporal variation of sound speed,in seafloor geodetic precise positioning,can always be attributed to the time error.Firstly,this paper analyzes the existing error compensation model,i.e.,the time ratio model,which is expressed by the recorded time multiplying a ratio coefficient.And then a time split model is proposed by expressing the acoustic ray traveling time as the recorded time pluses a perturbation time error.The theoretical differences between the proposed time bias compensation model and the time ratio model are analyzed.Under the new framework,sound speed perturbation models with optimal single-layer spatial gradient and multi-layer spatial gradients are developed to compensate for sound speed error in the complex cases.Numerical computation shows that the simple time split model keeps the same accuracy as some complicated models while considering the distribution of random error.Furthermore,multi-layer model can improve the positioning accuracy without putting the pressure on parametrization.
基金The National Key Research and Development Program under contract No. 2024YFB3909702the National Natural Science Foundation of China under contract Nos 42474014, 41931076, and 42388102+2 种基金the Scientific and Technology Inmo-vation Program of Laoshan Laboratory under contract Nos LSKJ202205100 and LSKJ202205105the Independent Research Project of State Key Laboratory of Geo-information Engineering under contract SKLGIE2023-ZZ-8the Scientific Research Project of Chinese Academy of Surveying and Mapping under contract No. AR2501。
文摘The spatiotemporal variations of sound speed, particularly the drastic variation in depth, significantly affect seafloor geodetic positioning precision. For this reason, the global navigation satellite system-acoustic(GNSS-A) positioning technology typically uses in-situ sound speed profiles(SSPs) and considers the impact of these variations at the data post-processing stage. However, in-situ SSP measurement is costly and somewhat hinders the timeliness of seafloor geodetic monitoring. We generalize the bilinear SSP(BL-SSP) to be a piecewise-linear SSP, whose model parameters are estimated from GNSS-A observations. In addition, we construct a set of constraints based on a priori marine environment observation to stabilize SSP inversion and propose an algorithm to recursively conduct the inversion, e.g.,the trilinear SSP(TL-SSP) inversion is initialized using the BL-SSP inversion result. The proposed model is verified by long-term GNSS-A seafloor geodetic observations. It shows that the root mean square error(RMSE) of the TL-SSP inversion result is 10.87 m/s, compared to 11.08 m/s for the traditional BL-SSP, with significant improvements observed in shallow and middle water layers. Furthermore, when replacing the in-situ SSP with the inverted SSP for precise seafloor geodetic positioning and incorporating the acoustic delay parameters, the TL-SSP-based positioning demonstrates higher accuracy than the BL-SSP-based approach. Relative to the positioning result based on the in-situ SSP, the mean bias, standard deviation and RMSE of the horizontal positioning error are better than 0.003 m, 0.005 m,and 0.006 m, respectively, while those of the vertical positioning error are better than 0.03 m, 0.04 m, and 0.04 m,respectively. Compared with BL-SSP, TL-SSP can achieve a positioning error reduction along the E-direction, Ndirection, and U-direction by 16.7%, 15.0%, and 5.5%, respectively.
基金supported by the National Key Research and Development Program of China(2022YFA1404400)the National Natural Science Foundation of China(62122072,12174368,61705216,62405306)+4 种基金Anhui Provincial Department of Science and Technology(202203a07020020,18030801138)Anhui Provincial Natural Science Foundation(2308085QA21,2408085QF187)the USTC Research Funds of the Double First-Class Initiative(YD2090002015)the Institute of Artificial Intelligence at Hefei Comprehensive National Science Center(23YGXT005)the Fundamental Research Funds for the Central Universities(WK2090000083).
文摘Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.
基金Supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (No.KZCX1-YW-12-02)the National Natural Science Foundation of China (Nos.10974218,10734100)
文摘A sound speed profile plays an important role in shallow water sound propagation.Concurrent with in-situ measurements,many inversion methods,such as matched-field inversion,have been put forward to invert the sound speed profile from acoustic signals.However,the time cost of matched-field inversion may be very high in replica field calculations.We studied the feasibility and robustness of an acoustic tomography scheme with matched-field processing in shallow water,and described the sound speed profile by empirical orthogonal functions.We analyzed the acoustic signals from a vertical line array in ASIAEX2001 in the East China Sea to invert sound speed profiles with estimated empirical orthogonal functions and a parallel genetic algorithm to speed up the inversion.The results show that the inverted sound speed profiles are in good agreement with conductivity-temperature-depth measurements.Moreover,a posteriori probability analysis is carried out to verify the inversion results.
基金The National Natural Science Foundation of China under contract Nos 42076082,41706062 and 41676055the Director Fund of Pilot National Laboratory for Marine Science and Technology(Qingdao)under contract No.QNLM201713+1 种基金the Public Science and Technology Research Funds Projects of Ocean under contract No.201405032the Taishan Scholar Project Funding under contract No.tspd20161007。
文摘Building empirical equations is an effective way to link the acoustic and physical properties of sediments.These equations play an important role in the prediction of sediments sound speeds required in underwater acoustics.Although many empirical equations coupling acoustic and physical properties have been developed over the past few decades,further confirmation of their applicability by obtaining large amounts of data,especially for equations based on in situ acoustic measurement techniques,is required.A sediment acoustic survey in the South Yellow Sea from 2009 to 2010 revealed statistical relationships between the in situ sound speed and sediment physical properties.To improve the comparability of these relationships with existing empirical equations,the present study calculated the ratio of the in situ sediment sound speed to the bottom seawater sound speed,and established the relationships between the sound speed ratio and the mean grain size,density and porosity of the sediment.The sound speed of seawater at in situ measurement stations was calculated using a perennially averaged seawater sound speed map by an interpolation method.Moreover,empirical relations between the index of impedance and the sound speed and the physical properties were established.The results confirmed that the existing empirical equations between the in situ sound speed ratio and the density and porosity have general suitability for application.This study also considered that a multiple-parameter equation coupling the sound speed ratio to both the porosity and the mean grain size may be more useful for predicting the sound speed than an equation coupling the sound speed ratio to the mean grain size.
基金National Natural Science Foundation of China(Nos.41931076,42174020)Laoshan Laboratory(No.LSKJ202205101)State Key Laboratory of Geo-Information Engineering(No.SKLGIE2020-M-1-1)。
文摘At present,GNSS-Acoustic(GNSS-A)combined technology is widely used in positioning for seafloor geodetic stations.Based on Sound Velocity Profiles(SVPs)data,the equal gradient acoustic ray-tracing method is applied in high-precision position inversion.However,because of the discreteness of the SVPs used in the forementioned method,it ignores the continuous variation of sound velocity structure in time domain,which worsens the positioning accuracy.In this study,the time-domain variation of Sound Speed Structure(SSS)has been considered,and the cubic B-spline function is applied to characterize the perturbed sound velocity.Based on the ray-tracing theory,an inversion model of“stepwise iteration&progressive corrections”for both positioning and sound speed information is proposed,which conducts the gradual correction of seafloor geodetic station coordinates and disturbed sound velocity.The practical data was used to test the effectiveness of our method.The results show that the Root Mean Square(RMS)errors of the residual values of the traditional methods without sound velocity correction,based on quadratic polynomial correction and based on cubic B-spline function correction are 1.43 ms,0.44 ms and 0.21 ms,respectively.The inversion model with sound velocity correction can effectively eliminate the systematic error caused by the change of SSS,and significantly improve the positioning accuracy of the seafloor geodetic stations.
基金supported by the project funded by the National Natural Science Foundation of China(Nos.41906160,11974286 and 12174312).
文摘Traditional acquisition method of sound speed profiles using hydro-acoustic instruments is accurate but time-consuming and costly.To overcome this problem,some inversion methods have been developed over the last few decades.In this study,a comprehensive comparison of two inversion methods–the acoustic inversion method(AIM)and the satellite observation reconstruction method(SOR)–is presented.For AIM,the sound speed profile is first parameterized by the empirical orthogonal function(EOF)and the optimal parameters are searched by simulated annealing algorithm with respect to the cross-correlation function of the receiving signal and the simulation signal.For SOR,remotely sensed data are used to construct sound speed profiles.An experiment was conducted in the northeast of the South China Sea to verify the two methods.Both methods can obtain sound speed profiles quickly and cheaply.Compared with the sound speed profiles obtained by a conductivity-temperature-depth(CTD)instrument,the root-meansquare-error(RMSE)of AIM is 0.55 m s^(−1) and that of SOR is 1.71 m s^(−1).It is clear that AIM provides better inversion performance than SOR.Another primary benefit of AIM is that this method has no limitation to the inversion depth.The simulation results of sound propagation in regard to the inversed sound speed profiles show that the transmission losses of AIM and CTD are consistent and that of SOR is adversely affected by the inversion error of the sound speed and the inversion depth.But SOR has particular advantages in the inversion coverage.Together,all of these advantages make the AIM particularly valuable in practice.
基金Supported by the National Natural Science Foundation of China(No.42107157)the Laboratory for Marine Geology,Qingdao National Laboratory for Marine Science and Technology(No.MGQNLM-KF202101)+1 种基金the Fundamental Research Funds for the Central Universities,SCUT(No.21CX06016A)the Harbin Engineering University at Qingdao(No.2022-SXZN-CXJJ-04-06+01)。
文摘With the consumption of terrestrial metal resources,the exploitation of deep-sea polymetallic nodule minerals has been widely concerned around the world.Therefore,the environmental impact of deep-sea polymetallic nodule mining cannot be ignored.However,duo to the lacks in stable and safe deep-sea(the depth>1000 m)vertical profile observation systems and consequently in long-term in-situ observation data,the sound speed and dissolved oxygen and the other water environment factors in the deposition areas of polymetallic nodules remains poorly understood.In this study,a deep-sea in-situ observation system was designed and deployed,and the water environment data of the polymetallic nodule deposition area were collected and analyzed.Result shows that the dissolved oxygen in the depth of 0–600 m was mainly affected by biological factors,while that in the area deeper than 600 m was affected by physical factors.The sound speed in the water body was mainly affected by temperature and pressure.At depths below 840 m,the sound speed is mainly controlled by temperature,and at depths between 840 m and 5700 m,the sound speed is mainly controlled by pressure.The correlations of sound speed vs.pressure and vs.temperature were regressed into equation.The resuspension of sediments rich in various metals may result in the reduction of dissolved oxygen and the improvement of redox potential.This environmental impact caused by a single sediment resuspension could last for 24 h or more.These findings enrich the understanding of the background value of the water environment in the polymetallic nodule deposition area.
基金Supported by the National Natural Science Foundation of China(No.41876014)the Open Project of Tianjin Key Laboratory of Oceanic Meteorology(No.2020TKLOMYB04)。
文摘It is essential to ac quire sound speed profiles(SSPs)in high-precision spatiotemporal resolution for undersea acoustic activities.However,conventional observation methods cannot obtain high-resolution SSPs.Besides,S SPs are complex and changeable in time and space,especially in coastal areas.We proposed a new space-time multigrid three-dimensional variational method with weak constraint term(referred to as STC-MG3DVar)to construct high-precision spatiotemporal resolution SSPs in coastal areas,in which sound velocity is defined as the analytical variable,and the Chen-Millero sound velocity empirical formula is introduced as a weak constraint term into the cost function of the STC-MG3DVar.The spatiotemporal correlation of sound velocity observations is taken into account in the STC-MG3DVar method,and the multi-scale information of sound velocity observations from long waves to short waves can be successively extracted.The weak constraint term can optimize sound velocity by the physical relationship between sound velocity and temperature-salinity to obtain more reasonable and accurate SSPs.To verify the accuracy of the STC-MG3DVar,SSPs observations and CTD observations(temperature observations,salinity observations)are obtained from field experiments in the northern coastal area of the Shandong Peninsula.The average root mean square error(RMSE)of the STC-MG3DVar-constructed SSPs is 0.132 m/s,and the STC-MG3DVar method can improve the SSPs construction accuracy over the space-time multigrid 3DVar without weak constraint term(ST-MG3DVar)by 10.14%and over the spatial multigrid 3DVar with weak constraint term(SC-MG3DVar)by 44.19%.With the advantage of the constraint term and the spatiotemporal correlation information,the proposed STC-MG3DVar method works better than the ST-MG3DVar and the SCMG3DVar in constructing high-precision spatiotemporal re solution SSPs.
基金Supported by the National Natural Science Foundation of China under Grant No 51506051the National Basic Research Program of China under Grant No 2015CB251503the Fundamental Research Funds for the Central Universities under Grant No JB2015RCY04
文摘There are numerous formulae relating to the predictions of sound wave in the cavitating and bubbly flows. However, tile valid regions of those formulae are rather unclear from the view point of physics. In this work, the validity of the existing formulae is discussed in terms of three regions by employing the analysis of three typical lengths involved （viscous length, thermal diffusion length and bubble radius）. In our discussions, viscosity and thermal diffusion are both considered together with the effects of relative motion between bubbles and liquids. The importance of relative motion and thermal diffusion are quantitatively discussed in a wide range of parameter zones （including bubble radius and acoustic frequency）, The results show that for large bubbles, the effects of relative motion will be prominent in a wide region.
基金The Natural Science Foundation of Guangdong Province under contract No.2022A1515011519the National Natural Science Foundation of China under contract No.11904290.
文摘Complex perturbations in the profile and the sparsity of samples often limit the validity of rapid environmental assessment(REA)in the South China Sea(SCS).In this paper,the remote sensing data were used to estimate sound speed profile(SSP)with the self-organizing map(SOM)method in the SCS.First,the consistency of the empirical orthogonal functions was examined by using k-means clustering.The clustering results indicated that SSPs in the SCS have a similar perturbation nature,which means the inverted grid could be expanded to the entire SCS to deal with the problem of sparsity of the samples without statistical improbability.Second,a machine learning method was proposed that took advantage of the topological structure of SOM to significantly improve their accuracy.Validation revealed promising results,with a mean reconstruction error of 1.26 m/s,which is 1.16 m/s smaller than the traditional single empirical orthogonal function regression(sEOF-r)method.By violating the constraints of linear inversion,the topological structure of the SOM method showed a smaller error and better robustness in the SSP estimation.The improvements to enhance the accuracy and robustness of REA in the SCS were offered.These results suggested a potential utilization of REA in the SCS based on satellite data and provided a new approach for SSP estimation derived from sea surface data.
基金The National Natural Science Foundation of China under contract No.11704225the Shandong Provincial Natural Science Foundation under contract No.ZR2016AQ23+3 种基金the State Key Laboratory of Acoustics,Chinese Academy of Sciences under contract No.SKLA201902the National Key Research and Development Program of China contract No.2018YFC1405900the SDUST Research Fund under contract No.2019TDJH103the Talent Introduction Plan for Youth Innovation Team in Universities of Shandong Province(Innovation Team of Satellite Positioning and Navigation)
文摘The estimation of ocean sound speed profiles(SSPs)requires the inversion of an acoustic field using limited observations.Such inverse problems are underdetermined,and require regularization to ensure physically realistic solutions.The empirical orthonormal function(EOF)is capable of a very large compression of the data set.In this paper,the non-linear response of the sound pressure to SSP is linearized using a first order Taylor expansion,and the pressure is expanded in a sparse domain using EOFs.Since the parameters of the inverse model are sparse,compressive sensing(CS)can help solve such underdetermined problems accurately,efficiently,and with enhanced resolution.Here,the orthogonal matching pursuit(OMP)is used to estimate range-independent acoustic SSPs using the simulated acoustic field.The superior resolution of OMP is demonstrated with the SSP data from the South China Sea experiment.By shortening the duration of the training set,the temporal correlation between EOF and test sets is enhanced,and the accuracy of sound velocity inversion is improved.The SSP estimation error versus depth is calculated,and the 99%confidence interval of error is within±0.6 m/s.The 82%of mean absolute error(MAE)is less than 1 m/s.It is shown that SSPs can be well estimated using OMP.
文摘Ocean sound speed profile(SSP) is the key factor affecting acoustic propagation. The acquisition of SSPsin real time with high precision is meaningful for underwater activities. By means of the remote sensing method, thesea surface data could be obtained in near-real time. Typically, the subsurface fields are correlated with the sea surfaceparameters. Thus, the SSPs could be obtained by means of satellite remote sensing. In this paper, the history as wellas the current research over the reconstruction of subsurface fields by means of sea surface data is introduced. Thentwo methods to reconstruct the SSPs with sea surface data, including the linear regression method using the empiricalorthogonal function, and the self-organizing method based on the big data theory, are described in detail in the paper.
文摘Purpose: A novel image-based method for speed of sound (SoS) estimation is proposed and experimentally validated on a tissue-mimicking ultrasound phantom and normal human liver in vivo using linear and curved array transducers. Methods: When the beamforming SoS settings are adjusted to match the real tissue’s SoS, the ultrasound image at regions of interest will be in focus and the image quality will be optimal. Based on this principle, both a tissue-mimicking ultrasound phantom and normal human liver in vivo were used in this study. Ultrasound image was acquired using different SoS settings in beamforming channels ranging from 1420 m/sec to 1600 m/sec. Two regions of interest (ROIs) were selected. One was in a fully developed speckle region, while the other contained specular reflectors. We evaluated the image quality of these two ROIs in images acquired at different SoS settings in beamforming channels by using the normalized autocorrelation function (ACF) of the image data. The values of the normalized ACF at a specific lag as a function of the SoS setting were computed. Subsequently, the soft tissue’s SoS was determined from the SoS setting at the minimum value of the normalized ACF. Results: The value of the ACF as a function of the SoS setting can be computed for phantom and human liver images. SoS in soft tissue can be determined from the SoS setting at the minimum value of the normalized ACF. The estimation results show that the SoS of the tissue-mimicking phantom is 1460 m/sec, which is consistent with the phantom manufacturer’s specification, and the SoS of the normal human liver is 1540 m/sec, which is within the range of the SoS in a healthy human liver in vivo. Conclusion: Soft tissue’s SoS can be determined by analyzing the normalized ACF of ultrasound images. The method is based on searching for a minimum of the normalized ACF of ultrasound image data with a specific lag among different SoS settings in beamforming channels.
基金supported by the Science and Technology Innovation Project Funded by Laoshan Laboratory(LSKJ202205102)the Basic Scientifc Fund for National Public Research Institutes of China(2022S03)+2 种基金the National Key Research and Development Program of China(2020YFB0505805)the National Natural Science Foundation of China(42004030)the Shandong Provincial Natural Science Foundation(ZR2023QD179).
文摘The Global Navigation Satellite System–Acoustic(GNSS-A)combined positioning technique extends geodetic networks into the seafoor.Currently,GNSS-A can achieve static seafoor positioning accuracy at centimeter level.However,in practical operations,substantial time,manpower,fnancial and instrument resources are required to measure in situ Sound Speed Profles(SSPs).This paper evaluates the feasibility of GNSS-A with alternative SSPs instead of in situ measurements.The GNSS-A positioning using three diferent SSPs are compared:the Munk empirical profle,the profles from the HYbrid Coordinate Ocean Model(HYCOM)global ocean analysis product,and the in situ profles.Compared with the in situ profle,the Munk SSP has little impact on the GNSS-A horizontal position(0.6 cm in root-mean-square,RMS)but introduces a large systematic error in the vertical position(10.3 cm in RMS),and the impact on the displacement velocity is at the mm/a level.When the HYCOM profle is substituted for in situ profles,the impact on GNSS-A positioning is only 0.2 cm in the horizontal and 2.9 cm in the vertical,and the impact on displacement velocity is at the sub-mm/a level in the horizontal and mm/a level in the vertical.The HYCOM global ocean analysis SSPs can largely serve as a cost-efective substitute for in situ profles in GNSS-A seafoor positioning,which is especially applicable to GNSS-A measurements using unmanned surface vehicles,for which full-depth SSP measurements are difcult.Therefore,when SSPs are selected,appropriate decisions should be made on the basis of specifc GNSS-A application needs and conditions.
基金National Natural Science Foundation of China(42304040,42174020,42174021)National Key Research and Development Program of China(No.2024YFB3909700,2024YFB3909702)+3 种基金Shandong Province Natural Science Foundation(ZR2023QD081,ZR2025MS643)National Key Laboratory of Spatial Datum(No.SKLSD2025-KF-16)Fundamental Research Funds for the Central Universities(No.24CX06045A)Qingdao Natural Science Foundation(23-2-1-65-zyyd-jch,23-2-1-217-zyyd-jch).
文摘Autonomous and Remotely-operated Vehicles(ARVs)rely on precise underwater navigation via integrated Ultra-Short Baseline(USBL)acoustic positioning system and Strap-down Inertial Navigation System(SINS).However,spatiotemporal variations in underwater Sound Speed Profle(SSP)degrade USBL performance,reducing overall navigation accuracy.This study proposes a novel in-situ SSP correction scheme for SINS/USBL integration.We analyze SSP temporal variation with the USBL positioning scheme to build a Two Dimensional(2D)temporal SSP model;then derive partial derivatives(based on equal-gradient ray-tracing)to quantify the displacements from azimuth,incident angle,and propagation time errors;and fnally develop an adaptive two-stage information flter to estimate sound speed perturbation and detect USBL outliers.Simulations and South China Sea trials are conducted to verify its efectiveness.Compared with the traditional tight-coupling method,root mean square errors are reduced from 0.45m and 0.23 m with the traditional tightly-coupled method to 0.08 m and 0.07 m with the in-situ SSP correction scheme,representing improvements of 82.2%in the north and 69.6%in the east directions,respectively.Experimental results demonstrate that the proposed method efectively estimates the sound speed disturbance in real time,thereby signifcantly improving the performance of tightly integrated inertial-acoustic navigation systems.
基金supported by the National Natural Science Foundation of China(62371404,62271425,62071401).
文摘To address the problem of underwater sound speed profile(SSP)inversion in underwater acoustic multipath channels,this paper combines deep learning and ray theory to propose an inversion method using a long short-term memory(LSTM)network.Based on the equidistant characteristics of the horizontal line array,the proposed method takes the sensing matrix composed of multi-modal data,such as time difference of arrival and angle of arrival,as input,and utilizes the ability of the LSTM network to process timeseries data to mine the correlations between spatially ordered receiving array elements for sound speed profile inversion.On this basis,a time delay estimation method based on hard threshold estimation method and cross-correlation function is proposed to reduce the measurement errors of the sensing matrix and improve the anti-multipath performance.The feasibility and accuracy of the proposed method are verified through numerical simulations.Compared with the traditional optimization algorithm,the proposed algorithm better captures the nonlinear characteristics of SSP,with higher inversion accuracy and stronger noise resistance.
基金This study was financially supported by National Natural Science Foundation of China(41931076)Laoshan Laboratory(LSKJ202205100,LSKJ202205105)The Special Fund of Chinese Central Government for Basic Scientific Research Operations(AR2115).
文摘In-field Sound Speed Profile(SSP)measurement is still indispensable for achieving centimeter-level-precision Global Navigation Satellite System(GNSS)-Acoustic(GNSS-A)positioning in current state of the art.However,in-field SSP measurement on the one hand causes a huge cost and on the other hand prevents GNSS-A from global seafloor geodesy especially for real-time applications.We propose an Empirical Sound Speed Profile(ESSP)model with three unknown temperature parameters jointly estimated with the seafloor geodetic station coordinates,which is called the 1st-level optimization.Furthermore,regarding the sound speed variations of ESSP we propose a so-called 2nd-level optimization to achieve the centimeter-level-precision positioning for monitoring the seafloor tectonic movement.Long-term seafloor geodetic data analysis shows that,the proposed two-level optimization approach can achieve almost the same positioning result with that based on the in-field SSP.The influence of substituting the in-field SSP with ESSP on the horizontal coordinates is less than 3 mm,while that on the vertical coordinate is only 2–3 cm in the standard deviation sense.
基金supported by the National Natural Science Foundation of China(41576103)
文摘An approach for time-evolving sound speed profiles tracking in shallow water is discussed. The inversion of time-evolving sound speed profiles is modeled as a state-space estimation problem, which includes a state equation for predicting the time-evolving sound speed profile and a measurement equation for incorporating local acoustic measurements. In the paper, auto-regression （AR） method is introduced to obtain a high-order AR evolution model of the sound speed field time variations, and the ensemble Kalman filter is utilized to track the sound speed field. To validate the approach, the accuracy in sound speed estimation is analyzed via a numerical implementation using the ASIAEX experimental environment and the sound velocity measurement data. Compared with traditional approaches based on the state evolution represented as a random walk, simulation results show the proposed AR method can effectively reduce the tracking errors of sound speed, and still keep good tracking performance at low signal-to-noise ratios.
基金Project supported by the National Key Research and Development Program of China(No.2017YFC0305900)the Zhejiang University K.P.Chao’s High Technology Development Foundation(No.2020ZL013)。
文摘In the underwater medium,the speed of sound varies with water depth,temperature,and salinity.The inhomogeneity of water leads to bending of sound rays,making the existing localization algorithms based on straightline propagation less precise.To realize high-precision node positioning in underwater acoustic sensor networks(UASNs),a multi-layer isogradient sound speed profile(SSP)model is developed using the linear segmentation approximation approach.Then,the sound ray tracking problem is converted into a polynomial root-searching problem.Based on the derived gradient of the signal’s Doppler shift at the sensor node,a novel underwater node localization algorithm is proposed using both the time difference of arrival(TDOA)and frequency difference of arrival(FDOA).Simulations are implemented to illustrate the effectiveness of the proposed algorithm.Compared with the traditional straight-line propagation method,the proposed algorithm can effectively handle the sound ray bending phenomenon.Estimation accuracy with different SSP modeling errors is also investigated.Overall,accurate and reliable node localization can be achieved.
