In this paper,the newly-derived maximum correntropy Kalman filter(MCKF)is re-derived from the M-estimation perspective,where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel functi...In this paper,the newly-derived maximum correntropy Kalman filter(MCKF)is re-derived from the M-estimation perspective,where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions.Based on the derivation process,a unified form for the robust Gaussian filters(RGF)based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement.The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals.Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust antijamming performance.展开更多
Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring...Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.展开更多
Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty...Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points.The paper analyzes the characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed.展开更多
The corrosion process of copper in thermal flow system was investigated through the experimental bench.According to surface variation of samples during corrosion process,the surface model of specimen was build up base...The corrosion process of copper in thermal flow system was investigated through the experimental bench.According to surface variation of samples during corrosion process,the surface model of specimen was build up based on Gaussian filter.The results show that the corrosion characterization of copper in thermal flow system is pitting corrosion.The morphology characterizations of metal corrosion process can be described using the proposed surface model.The generation and development of copper pitting process can be observed clearly.展开更多
An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notifica...An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.展开更多
In this paper, the spatial gravity distribution over Tibetan Plateau and the gravity rate of change at Lhasa for different Gaussian filter radii are computed using GRACE data. Results show that the estimate of the gra...In this paper, the spatial gravity distribution over Tibetan Plateau and the gravity rate of change at Lhasa for different Gaussian filter radii are computed using GRACE data. Results show that the estimate of the gravity rate of change is spatialradius-dependent of the Ganssian filter. The GRACE-estimated gravity rate of change agrees well with the surface measured one. In other words, the GRACE-estimated gravity rate of change has a limited value as that obtained by surface measurement when the spatial filter radius reaches zero. Then numerical simulations are made for different spatial radii of the Gaussian filter to investigate its behaviors when applied to surface signals. Results show that the estimate of a physical signal is filter-radius dependent. If the computing area is equal to or less than the mass area, especially for a uniformly distributed mass, the estimate gives an almost correct result, no matter what filter radius is used. The estimate has large error because of the signal leakage caused by harmonic truncation if the computing area is much bigger than the mass distribution （or inverse for a small mass anomaly）. If a mass anomaly is too small, it is difficult to recover it from space observation unless the filter radius is extremely small. If the computing point （or area） is outside the mass distribution, the estimated result is almost zero, particularly for small filter radii. These properties of the Gaussian filter are helpful in applying GRACE data in different geophysical problems with different spatial position and geometrical size. We further discuss physical sources causing the scalar gravity change at Lhasa. Discussions indicate that the gravity rate of change at Lhasa is not caused by the present-day ice melting （PDIM） （or Little Ice Age, LIA） effect because no ice melting occurs in Lhasa city and nearby. The gravity rate of change is attributable mainly to tectonic deformation associated with the Indian Plate collision. Simultaneous surface displacement, surface denudation, and GIA effects are not negligible.展开更多
Currently, the approximation methods of the Gaussian filter by some other spline filters have been developed. However, thesc methods are only suitable for the study of one-dimensional filtering, when these methods are...Currently, the approximation methods of the Gaussian filter by some other spline filters have been developed. However, thesc methods are only suitable for the study of one-dimensional filtering, when these methods are used for three-dimensional filtering, it is found that a rounding error and quantization error would be passed to the next in every part. In this paper, a new and high-precision implementation approach for Gaussian filter is described, which is suitable for three-dimensional reference filtering. Based on the theory of generalized B-spline function and the variational principle, the transmission characteristics of a digital filter can be changed through the sensitivity of the parameters （t1, t2）, and which can also reduce the rounding error and quantization error by the filter in a parallel form instead of the cascade form, Finally, the approximation filter of Gaussian filter is obtained. In order to verify the feasibility of the new algorithm, the reference extraction of the conventional methods are also used and compared. The experiments are conducted on the measured optical surface, and the results show that the total calculation by the new algorithm only requires 0.07 s for 480×480 data points; the amplitude deviation between the reference of the parallel form filter and the Gaussian filter is smaller; the new method is closer to the characteristic of the Gaussian filter through the analysis of three-dimensional roughness parameters, comparing with the cascade generalized B-spline approximating Gaussian. So the new algorithm is also efficient and accurate for the implementation of Gaussian filter in the application of surface roughness measurement.展开更多
This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise den...This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise density.To adaptively deal with variety of the noise amount,a noisy input image is firstly filtered by a lowpass filter.Standard deviation of the noise is computed from different images between the noisy input and its filtered image.In addition,a modified Gaussian noise removal filter based on the local statistics such as local weighted mean,local weighted activity and local maximum is used to control the degree of noise suppression.Experiments show the effectiveness of the proposed algorithm.展开更多
In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filt...In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filter is proposed.However,ISO16610-31:Gaussian regression filters(GRF)only provide a single method and a few examples,and does not specify the conditions under which the primary profile can be covered.Moreover,the data presented in the example on robustness in ISO16610-31 do not contain roughness components.In actual roughness measurements,no primary profile exists that does not include a roughness component.Because the characteristics of GRFs are unknown,it is not yet clear which filter should be used for which primary profile,and this is an issue that has been raised at ISO and JIS conferences.In addition,the establishment of filter selection guidelines is necessary at measurement sites.Therefore,this paper clarifies the characteristics of GF-series filters,summarizes the points to be considered when using them,and identifies the filter that should be selected according to different situations.Based on the results,a figure that visualizes the characteristics of filters and a flowchart regarding which filter should be used are created;these tools,to the best of the authors’knowledge,did not exist prior to this study.It is believed that these results will help fulfil the needs of measuring job sites and also aid in filter selection.展开更多
Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing ...Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing traditional methods face several significant challenges,including low background suppression ability,low detection rates,and high false alarm rates when identifying infrared small targets in complex environments.This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach.The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.In the first step,the infrared image is filtered by a new filter kernel and the results of filtering are normalized.In the second step,an adaptive thresholding method is utilized to determine the pixels in small targets.In the final step,a fuzzy C-mean clustering algorithm is employed to group pixels in the same target,thus yielding the detection results.The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches.Compared with the traditional method of state of the arts detection method,the detection accuracy of the four sequences is increased by 2.06%,0.95%,1.03%,and 1.01%,respectively,and the false alarm rate is reduced,thus providing a more effective and robust solution.展开更多
The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cub...The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.展开更多
With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved ...With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.展开更多
To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algori...To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function.KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly.Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly.Compared with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions.展开更多
Determination of relative three-dimensional （3D） position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fi...Determination of relative three-dimensional （3D） position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional （2D） intensity images from a single camera is desirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter （GPF） based on the panicle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter （EKF） and the unscented Kalman filter （UKF） to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.展开更多
In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into...In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.展开更多
The particle filter （PF） is a flexible and powerful sequen- tial Monte Carlo （SMC） technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from p...The particle filter （PF） is a flexible and powerful sequen- tial Monte Carlo （SMC） technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im- poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model （IAGM） based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser- vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im- prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.展开更多
This paper is concerned with a filtering problem for a class of nonlinear quantum stochastic systems with multichannel nondemolition measurements. The system-observation dynamics are governed by a Markovian Hudson-Par...This paper is concerned with a filtering problem for a class of nonlinear quantum stochastic systems with multichannel nondemolition measurements. The system-observation dynamics are governed by a Markovian Hudson-Parthasarathy quantum stochastic differential equation driven by quantum Wiener processes of bosonic fields in vacuum state. The Hamiltonian and system-field coupling operators, as functions of the system variables, are assumed to be represented in a Weyl quantization form. Using the Wigner-Moyal phase-space framework, we obtain a stochastic integro-differential equation for the posterior quasi-characteristic function （QCF） of the system conditioned on the measurements. This equation is a spatial Fourier domain representation of the Belavkin-Kushner-Stratonovich stochastic master equation driven by the innovation process associated with the measurements. We discuss a specific form of the posterior QCF dynamics in the case of linear system-field coupling and outline a Gaussian approximation of the posterior quantum state.展开更多
Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the ma...Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system.展开更多
基金supported by the Basic Science Center Program of the National Natural Science Foundation of China(62388101)the National Natural Science Foundation of China(61873275).
文摘In this paper,the newly-derived maximum correntropy Kalman filter(MCKF)is re-derived from the M-estimation perspective,where the MCKF can be viewed as a special case of the M-estimations and the Gaussian kernel function is a special case of many robust cost functions.Based on the derivation process,a unified form for the robust Gaussian filters(RGF)based on M-estimation is proposed to suppress the outliers and non-Gaussian noise in the measurement.The RGF provides a unified form for one Gaussian filter with different cost functions and a unified form for one robust filter with different approximating methods for the involved Gaussian integrals.Simulation results show that RGF with different weighting functions and different Gaussian integral approximation methods has robust antijamming performance.
文摘Filtering is a recursive estimation of hidden states of a dynamic system from noisy measurements.Such problems appear in several branches of science and technology,ranging from target tracking to biomedical monitoring.A commonly practiced approach of filtering with nonlinear systems is Gaussian filtering.The early Gaussian filters used a derivative-based implementation,and suffered from several drawbacks,such as the smoothness requirements of system models and poor stability.A derivative-free numerical approximation-based Gaussian filter,named the unscented Kalman filter(UKF),was introduced in the nineties,which offered several advantages over the derivativebased Gaussian filters.Since the proposition of UKF,derivativefree Gaussian filtering has been a highly active research area.This paper reviews significant developments made under Gaussian filtering since the proposition of UKF.The review is particularly focused on three categories of developments:i)advancing the numerical approximation methods;ii)modifying the conventional Gaussian approach to further improve the filtering performance;and iii)constrained filtering to address the problem of discrete-time formulation of process dynamics.This review highlights the computational aspect of recent developments in all three categories.The performance of various filters are analyzed by simulating them with real-life target tracking problems.
基金Supported by the National Natural Science Foundation of China (No.40871211)
文摘Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology.However,it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points.The paper analyzes the characteristics of Laplacian of Gaussian(LoG) Filter and its potential use for structure detection in LiDAR data.A feature detection method based on LoG filtering is presented and ex-perimented on the unstructured points.The method filters the elevation value(namely,z coordinate value) of each point by convo-lution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground ob-jects/features.The experiments are carried on a point cloud dataset acquired from a neighborhood area.The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size,which specifies the local area that is analyzed.
基金Project(2008DFR60340)supported by the International Science and Technology Cooperation of ChinaProject(2008087)supported by Scientific Technological Project of Weihai,China。
文摘The corrosion process of copper in thermal flow system was investigated through the experimental bench.According to surface variation of samples during corrosion process,the surface model of specimen was build up based on Gaussian filter.The results show that the corrosion characterization of copper in thermal flow system is pitting corrosion.The morphology characterizations of metal corrosion process can be described using the proposed surface model.The generation and development of copper pitting process can be observed clearly.
文摘An Intelligent Transportation System (ITS) is a new system developed for the betterment of user in traffic and transport management domain area for smart and safe driving. ITS subsystems are Emergency vehicle notification systems, Automatic road enforcement, Collision avoidance systems, Automatic parking, Map database management, etc. Advance Driver Assists System (ADAS) belongs to ITS which provides alert or warning or information to the user during driving. The proposed method uses Gaussian filtering and Median filtering to remove noise in the image. Subsequently image subtraction is achieved by subtracting Median filtered image from Gaussian filtered image. The resultant image is converted to binary image and the regions are analyzed using connected component approach. The prior work on speed bump detection is achieved using sensors which are failed to detect speed bumps that are constructed with small height and the detection rate is affected due to erroneous identification. And the smartphone and accelerometer methodologies are not perfectly suitable for real time scenario due to GPS error, network overload, real-time delay, accuracy and battery running out. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.
基金study was supported by NASA’s Interdisciplinary Science Program (Grant No. NNG04GN19G)the Ohio State University Climate, Water, and Carbon Program
文摘In this paper, the spatial gravity distribution over Tibetan Plateau and the gravity rate of change at Lhasa for different Gaussian filter radii are computed using GRACE data. Results show that the estimate of the gravity rate of change is spatialradius-dependent of the Ganssian filter. The GRACE-estimated gravity rate of change agrees well with the surface measured one. In other words, the GRACE-estimated gravity rate of change has a limited value as that obtained by surface measurement when the spatial filter radius reaches zero. Then numerical simulations are made for different spatial radii of the Gaussian filter to investigate its behaviors when applied to surface signals. Results show that the estimate of a physical signal is filter-radius dependent. If the computing area is equal to or less than the mass area, especially for a uniformly distributed mass, the estimate gives an almost correct result, no matter what filter radius is used. The estimate has large error because of the signal leakage caused by harmonic truncation if the computing area is much bigger than the mass distribution （or inverse for a small mass anomaly）. If a mass anomaly is too small, it is difficult to recover it from space observation unless the filter radius is extremely small. If the computing point （or area） is outside the mass distribution, the estimated result is almost zero, particularly for small filter radii. These properties of the Gaussian filter are helpful in applying GRACE data in different geophysical problems with different spatial position and geometrical size. We further discuss physical sources causing the scalar gravity change at Lhasa. Discussions indicate that the gravity rate of change at Lhasa is not caused by the present-day ice melting （PDIM） （or Little Ice Age, LIA） effect because no ice melting occurs in Lhasa city and nearby. The gravity rate of change is attributable mainly to tectonic deformation associated with the Indian Plate collision. Simultaneous surface displacement, surface denudation, and GIA effects are not negligible.
基金Supported by National Natural Science Foundation of China(Grant Nos51175085,51375094)Fujian Provincial Education Department Foundation of China(Grant No.JA13059)+1 种基金Open Fund of State Key Laboratory of Tribology of Tsinghua University,China(Grant No.SKLTKF13B02)Fuzhou Science and Technology plan Fund of China(Grant No.2014-G-74)
文摘Currently, the approximation methods of the Gaussian filter by some other spline filters have been developed. However, thesc methods are only suitable for the study of one-dimensional filtering, when these methods are used for three-dimensional filtering, it is found that a rounding error and quantization error would be passed to the next in every part. In this paper, a new and high-precision implementation approach for Gaussian filter is described, which is suitable for three-dimensional reference filtering. Based on the theory of generalized B-spline function and the variational principle, the transmission characteristics of a digital filter can be changed through the sensitivity of the parameters （t1, t2）, and which can also reduce the rounding error and quantization error by the filter in a parallel form instead of the cascade form, Finally, the approximation filter of Gaussian filter is obtained. In order to verify the feasibility of the new algorithm, the reference extraction of the conventional methods are also used and compared. The experiments are conducted on the measured optical surface, and the results show that the total calculation by the new algorithm only requires 0.07 s for 480×480 data points; the amplitude deviation between the reference of the parallel form filter and the Gaussian filter is smaller; the new method is closer to the characteristic of the Gaussian filter through the analysis of three-dimensional roughness parameters, comparing with the cascade generalized B-spline approximating Gaussian. So the new algorithm is also efficient and accurate for the implementation of Gaussian filter in the application of surface roughness measurement.
基金supported by the Korea Science and Engineering Foundation(KOSEF) grant fund by the Korea Govern-ment(MEST)(No.2011-0000148)the Ministry of Knowledge Economy,Korea under the Infor mation Technology Research Center support programsupervised by the National IT Industry Promotion Agency(NIPA-2011-C1090-1121-0010)
文摘This paper proposes a spatially denoising algorithm using filtering-based noise estimation for an image corrupted by Gaussian noise.The proposed algorithm consists of two stages:estimation and elimination of noise density.To adaptively deal with variety of the noise amount,a noisy input image is firstly filtered by a lowpass filter.Standard deviation of the noise is computed from different images between the noisy input and its filtered image.In addition,a modified Gaussian noise removal filter based on the local statistics such as local weighted mean,local weighted activity and local maximum is used to control the degree of noise suppression.Experiments show the effectiveness of the proposed algorithm.
基金We would like to thank Editage(www.editage.com)for English language editing.
文摘In surface roughness measurement,if spikes are included in the primary profile,a problem occurs wherein the Gaussian filter(GF)is unable to extract the shape components.To address this problem,the use of a robust filter is proposed.However,ISO16610-31:Gaussian regression filters(GRF)only provide a single method and a few examples,and does not specify the conditions under which the primary profile can be covered.Moreover,the data presented in the example on robustness in ISO16610-31 do not contain roughness components.In actual roughness measurements,no primary profile exists that does not include a roughness component.Because the characteristics of GRFs are unknown,it is not yet clear which filter should be used for which primary profile,and this is an issue that has been raised at ISO and JIS conferences.In addition,the establishment of filter selection guidelines is necessary at measurement sites.Therefore,this paper clarifies the characteristics of GF-series filters,summarizes the points to be considered when using them,and identifies the filter that should be selected according to different situations.Based on the results,a figure that visualizes the characteristics of filters and a flowchart regarding which filter should be used are created;these tools,to the best of the authors’knowledge,did not exist prior to this study.It is believed that these results will help fulfil the needs of measuring job sites and also aid in filter selection.
基金supported by the Funding of Jiangsu University of Science and Technology,under the grant number:1132921208.
文摘Infrared small target detection technology plays a pivotal role in critical military applications,including early warning systems and precision guidance for missiles and other defense mechanisms.Nevertheless,existing traditional methods face several significant challenges,including low background suppression ability,low detection rates,and high false alarm rates when identifying infrared small targets in complex environments.This paper proposes a novel infrared small target detection method based on a transformed Gaussian filter kernel and clustering approach.The method provides improved background suppression and detection accuracy compared to traditional techniques while maintaining simplicity and lower computational costs.In the first step,the infrared image is filtered by a new filter kernel and the results of filtering are normalized.In the second step,an adaptive thresholding method is utilized to determine the pixels in small targets.In the final step,a fuzzy C-mean clustering algorithm is employed to group pixels in the same target,thus yielding the detection results.The results obtained from various real infrared image datasets demonstrate the superiority of the proposed method over traditional approaches.Compared with the traditional method of state of the arts detection method,the detection accuracy of the four sequences is increased by 2.06%,0.95%,1.03%,and 1.01%,respectively,and the false alarm rate is reduced,thus providing a more effective and robust solution.
基金supported by the National Natural Science Foundation of China(No. 61032001)Shandong Provincial Natural Science Foundation of China (No. ZR2012FQ004)
文摘The paper deals with state estimation problem of nonlinear non-Gaussian discrete dynamic systems for improvement of accuracy and consistency. An efficient new algorithm called the adaptive Gaussian-sum square-root cubature Kalman filter（AGSSCKF） with a split-merge scheme is proposed. It is developed based on the squared-root extension of newly introduced cubature Kalman filter（SCKF） and is built within a Gaussian-sum framework. Based on the condition that the probability density functions of process noises and initial state are denoted by a Gaussian sum using optimization method, a bank of SCKF are used as the sub-filters to estimate state of system with the corresponding weights respectively, which is adaptively updated. The new algorithm consists of an adaptive splitting and merging procedure according to a proposed split-decision model based on the nonlinearity degree of measurement. The results of two simulation scenarios（one-dimensional state estimation and bearings-only tracking） show that the proposed filter demonstrates comparable performance to the particle filter with significantly reduced computational cost.
基金supported by the National Natural Science Foundation of China(61703228)
文摘With the increment of the number of Gaussian components, the computation cost increases in the Gaussian mixture probability hypothesis density(GM-PHD) filter. Based on the theory of Chen et al, we propose an improved pruning algorithm for the GM-PHD filter, which utilizes not only the Gaussian components’ means and covariance, but their weights as a new criterion to improve the estimate accuracy of the conventional pruning algorithm for tracking very closely proximity targets. Moreover, it solves the end-less while-loop problem without the need of a second merging step. Simulation results show that this improved algorithm is easier to implement and more robust than the formal ones.
基金the China Postdoctoral Science Foundation(No.171980)the National Natural Science Foundation of China(Nos.61973160,51505221)Key Laboratory Fund of Science and Technology on Communication Networks(No.6142104180114).
文摘To adjust the samples of filtering adaptively,an improved Gaussian particle filter algorithm based on Kullback-Leibler divergence(KLD)-sampling(KLGPF)is proposed in this paper.During the process of sampling,the algorithm calculates the KLD to adjust the size of the particle set between the discrete probability density function of particles and the true posterior probability density function.KLGPF has significant effect when the noise obeys Gaussian distribution and the statistical characteristics of noise change abruptly.Simulation results show that KLGPF could maintain a good estimation effect when the noise statistics changes abruptly.Compared with the particle filter algorithm using KLD-sampling(KLPF),the speed of KLGPF increases by 28%under the same conditions.
基金Project (No. 2006J0017) supported by the Natural Science Foundation of Fujian Province, China
文摘Determination of relative three-dimensional （3D） position, orientation, and relative motion between two reference frames is an important problem in robotic guidance, manipulation, and assembly as well as in other fields such as photogrammetry. A solution to pose and motion estimation problem that uses two-dimensional （2D） intensity images from a single camera is desirable for real-time applications. The difficulty in performing this measurement is that the process of projecting 3D object features to 2D images is a nonlinear transformation. In this paper, the 3D transformation is modeled as a nonlinear stochastic system with the state estimation providing six degrees-of-freedom motion and position values, using line features in image plane as measuring inputs and dual quaternion to represent both rotation and translation in a unified notation. A filtering method called the Gaussian particle filter （GPF） based on the panicle filtering concept is presented for 3D pose and motion estimation of a moving target from monocular image sequences. The method has been implemented with simulated data, and simulation results are provided along with comparisons to the extended Kalman filter （EKF） and the unscented Kalman filter （UKF） to show the relative advantages of the GPF. Simulation results showed that GPF is a superior alternative to EKF and UKF.
基金Project (No. 2011AA110304) supported by the National High-Tech R&D Program of China (863 program)
文摘In this paper,a prediction model is developed that combines a Gaussian mixture model(GMM) and a Kalman filter for online forecasting of traffic safety on expressways.Raw time-to-collision(TTC) samples are divided into two categories:those representing vehicles in risky situations and those in safe situations.Then,the GMM is used to model the bimodal distribution of the TTC samples,and the maximum likelihood(ML) estimation parameters of the TTC distribution are obtained using the expectation-maximization(EM) algorithm.We propose a new traffic safety indicator,named the proportion of exposure to traffic conflicts(PETTC),for assessing the risk and predicting the safety of expressway traffic.A Kalman filter is applied to forecast the short-term safety indicator,PETTC,and solves the online safety prediction problem.A dataset collected from four different expressway locations is used for performance estimation.The test results demonstrate the precision and robustness of the prediction model under different traffic conditions and using different datasets.These results could help decision-makers to improve their online traffic safety forecasting and enable the optimal operation of expressway traffic management systems.
基金supported by the National Natural Science Foundation of China(6127327561402517+3 种基金61573375)the Open Research Fund of State Key Laboratory of Astronautic Dynamics(2012ADL-DW0202)the Natural Science Foundation of Shaanxi Province of China(2013JQ8035)the China Postdoctoral Science Foundation(2013M542331)
文摘The particle filter （PF） is a flexible and powerful sequen- tial Monte Carlo （SMC） technique capable of modeling nonlinear, non-Gaussian, and nonstationary dynamical systems. However, the generic PF suffers from particle degeneracy and sample im- poverishment, which greatly affects its performance for nonlinear, non-Gaussian tracking problems. To deal with those issues, an improved PF is proposed. The algorithm consists of a PF that uses an immune adaptive Gaussian mixture model （IAGM） based immune algorithm to re-approximate the posterior density. At the same time, three immune antibody operators are embed in the new filter. Instead of using a resample strategy, the newest obser- vation and conditional likelihood are integrated into those immune antibody operators to update the particles, which can further im- prove the diversity of particles, and drive particles toward their close local maximum of the posterior probability. The improved PF algorithm can produce a closed-form expression for the posterior state distribution. Simulation results show the proposed algorithm can maintain the effectiveness and diversity of particles and avoid sample impoverishment, and its performance is superior to several PFs and Kalman filters.
文摘This paper is concerned with a filtering problem for a class of nonlinear quantum stochastic systems with multichannel nondemolition measurements. The system-observation dynamics are governed by a Markovian Hudson-Parthasarathy quantum stochastic differential equation driven by quantum Wiener processes of bosonic fields in vacuum state. The Hamiltonian and system-field coupling operators, as functions of the system variables, are assumed to be represented in a Weyl quantization form. Using the Wigner-Moyal phase-space framework, we obtain a stochastic integro-differential equation for the posterior quasi-characteristic function （QCF） of the system conditioned on the measurements. This equation is a spatial Fourier domain representation of the Belavkin-Kushner-Stratonovich stochastic master equation driven by the innovation process associated with the measurements. We discuss a specific form of the posterior QCF dynamics in the case of linear system-field coupling and outline a Gaussian approximation of the posterior quantum state.
基金Supported by the National Natural Science Foundation of China(No.61771006)the Open Foundation of Key Laboratory of Spectral Imaging Technology of the Chinese Academy of Sciences(No.LSIT201711D)+1 种基金the Outstanding Young Cultivation Foundation of Henan University(No.0000A40366)the Excellent Chinese and Foreign Youth Exchange Programme of China Science and Technology Association(2017CASTQNJL046)
文摘Aiming at improving the estimation accuracy and real-time of nonlinear system with linear Gaussian sub-structure,a novel marginalized cubature Kalman filter is proposed in Bayesian estimation framework. Firstly,the marginalized technique is adopted to model the target system dynamics with nonlinear state and linear state separately,and the two parts are estimated by cubature Kalman filter and standard Kalman filter respectively. Therefore,the linear part avoids the generation and propagation process of cubature points. Accordingly,the computational complexity is reduced.Meanwhile,the accuracy of state estimation is improved by taking the difference of nonlinear state estimation as the measurement of linear state. Furthermore,the computational complexity of marginalized cubature Kalman filter is discussed by calculating the number of floating-point operation. Finally,simulation experiments and analysis show that the proposed algorithm can improve the performance of filtering precision and real-time effectively in target tracking system.
