The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech ...The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech recognition. Combining the traditional direction finding technique of MUltiple SIgnal Classification (MUSIC) with the focusing matrix method, we improve the resolving power of the microphone array for multiple speech sources.As one application of finding Direction of Arrival (DOA), a new microphone-array system for noise reduction is proposed. The new system is based on maximum likelihood estimate technique which reconstruct superimposed signals from different directions by using DOA information. The DOA information is got in terms of focusing MUSIC method which has been proven to have high performance than conventional MUSIC method on speaker localization[1].展开更多
This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the b...This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.展开更多
Nonuniform linear arrays,such as coprime array and nested array,have received great attentions because of the increased degrees of freedom(DOFs)and weakened mutual coupling.In this paper,inspired by the existing copri...Nonuniform linear arrays,such as coprime array and nested array,have received great attentions because of the increased degrees of freedom(DOFs)and weakened mutual coupling.In this paper,inspired by the existing coprime array,we propose a high-order extended coprime array(HoECA)for improved direction of arrival(DOA)estimation.We first derive the closed-form expressions for the range of consecutive lags.Then,by changing the inter-element spacing of a uniform linear array(ULA),three cases are proposed and discussed.It is indicated that the HoECA can obtain the largest number of consecutive lags when the spacing takes the maximum value.Finally,by comparing it with the other sparse arrays,the optimized HoECA enjoys a larger number of consecutive lags with mitigating mutual coupling.Simulation results are shown to evaluate the superiority of HoECA over the others in terms of DOF,mutual coupling leakage and estimation accuracy.展开更多
When the information of mutual coupling and shadowing effect of a conformal antenna array are unknown, the performance of direction of arrival （DOA） estimation will be seriously degraded by using some classical meth...When the information of mutual coupling and shadowing effect of a conformal antenna array are unknown, the performance of direction of arrival （DOA） estimation will be seriously degraded by using some classical methods, such as the multiple signal classification （MUSIC） algorithm. Meanwhile it is difficult to measure or estimate the shadowing effect. The DOA estimation for a conformal uniform circular array （UCA） is studied. Firstly, the azimuthal angle is separated from all the unknown information by transforming the UCA from the element space to the mode space. Then the rank reduction （RARE） algorithm is applied in the estima- tion of the azimuthal angle. The π ambiguity existed in the RARE is solved by the beam forming. The main advantage of this method is that it does not need to measure the mutual coupling and the shadowing effect. Compared with the subarray method, it will not decrease the aperture of the array. Simulation results validate the advantages of the method.展开更多
A fiber Bragg grating (FBG) geophone and a surface seismic wave-based algorithm for detecting the direction of arrival (DOA) are described. The operational principle of FBG geophone is introduced and illustrated with ...A fiber Bragg grating (FBG) geophone and a surface seismic wave-based algorithm for detecting the direction of arrival (DOA) are described. The operational principle of FBG geophone is introduced and illustrated with systematic experimental data, demonstrating an improved FBG geophone with many advantages over the conventional geophones. An innovative, robust, and simple algorithm is developed for obtaining the bearing information on the seismic events, such as people walking, or vehicles moving. Such DOA estimate is based on the interactions and projections of surface-propagating seismic waves generated by the moving personnel or vehicles with a single tri-axial seismic sensor based on FBGs. Of particular interest is the case when the distance between the source of the seismic wave and the detector is less than or comparable to one wavelength (less than 100 m), corresponding to near-field detection, where an effective method of DOA finding lacks.展开更多
A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This l...A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This leads naturally to the derivation of minimum variance distortionless response(MVDR) algorithm, which combines the benefits of subspace methods with those of wavelet, and spatially smoothed versions are utilized which exhibits good performance against correlated signals. We test the method's performance by simulating and comparing the performance of proposed algorithm, FFT MVDR and MVDR with correlated signals, and an improved performance is obtained.展开更多
In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exp...In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel covariance matrix and obtain excellent performance via the maximumlikelihood(ML)algorithm.In order to obtain the global optimal solutions of this method,a quantum electromagnetic field optimization(QEFO)algorithm is designed.In view of the QEFO algorithm,the proposed method can resolve the difficulties of DOA estimation in the impulse noise.Comparing with some traditional DOA estimation methods,the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources,which has been verified via the Monte-Carlo experiments of different schemes,especially in the case of snapshot deficiency,low generalized signal to noise ratio(GSNR)and strong impulse noise.Beyond that,the Cramer-Rao bound(CRB)of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.展开更多
In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response...In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response to these issues,this paper introduces a novel,robust,and broadband cyclic beamforming algorithm.The proposed method substitutes the conventional cyclic covariance matrix with the variance of the cyclic covariance matrix as its primary feature.Assuming that the same frequency band shares a common steering vector,the new algorithm achieves superior detection performance for targets with specific modulation frequencies while suppressing interference signals and background noise.Experimental results demonstrate a significant enhancement in the directibity index by 81%and 181%when compared with the traditional Capon beamforming algorithm and the traditional extended wideband spectral cyclic MUSIC(EWSCM)algorithm,respectively.Moreover,the proposed algorithm substantially reduces computational complexity to 1/40th of that of the EWSCM algorithm,employing frequency band statistical averaging and covariance matrix variance.展开更多
According to the characteristic of the echo of highfrequency ground wave radar（HF GWR）, which is one-dimensional narrow band signal, a virtual direction of arrival（DOA） matrix is constructed at first, then the DOA...According to the characteristic of the echo of highfrequency ground wave radar（HF GWR）, which is one-dimensional narrow band signal, a virtual direction of arrival（DOA） matrix is constructed at first, then the DOA of target evaluation is achieved by the method of resolving equations for two-dimensional DOA matrix. And this method bases on the redundancy information of a linear two-row array of antennae. Both the simulation process and the treatment results of measured data （in the case of low SNR echoes and short data series） are given at the end of this paper. By comparing with GPS data of the targets, the validity and practical applicability of the method in this paper is verified.展开更多
Linear antenna arrays(LAs)can be used to accurately predict the direction of arrival(DOAs)of various targets of interest in a given area.However,under certain conditions,LA suffers from the problem of ambiguities amon...Linear antenna arrays(LAs)can be used to accurately predict the direction of arrival(DOAs)of various targets of interest in a given area.However,under certain conditions,LA suffers from the problem of ambiguities among the angles of targets,which may result inmisinterpretation of such targets.In order to cope up with such ambiguities,various techniques have been proposed.Unfortunately,none of them fully resolved such a problem because of rank deficiency and high computational cost.We aimed to resolve such a problem by proposing an algorithm using differential geometry.The proposed algorithm uses a specially designed doublet antenna array,which is made up of two individual linear arrays.Two angle observation models,ambiguous observation model(AOM)and estimated observation model(EOM),are derived for each individual array.The ambiguous set of angles is contained in the AOM,which is obtained from the corresponding array elements using differential geometry.The EOM for each array,on the other hand,contains estimated angles of all sources impinging signals on each array,as calculated by a direction-finding algorithm such as the genetic algorithm.The algorithm then contrasts the EOM of each array with its AOM,selecting the output of that array whose EOM has the minimum correlation with its corresponding AOM.In comparison to existing techniques,the proposed algorithm improves estimation accuracy and has greater precision in antenna aperture selection,resulting in improved resolution capabilities and the potential to be used more widely in practical scenarios.The simulation results using MATLAB authenticates the effectiveness of the proposed algorithm.展开更多
Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suf...Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.展开更多
Aiming at source number determination and direction of arrival(DOA) estimation under the case of time-varying source number,a method of DOA estimation with an unknown number of sources was proposed.Firstly,an algorith...Aiming at source number determination and direction of arrival(DOA) estimation under the case of time-varying source number,a method of DOA estimation with an unknown number of sources was proposed.Firstly,an algorithm based on crossvalidation technique was introduced to determine the number of sources.Then dynamic DOAs of source were estimated using an algorithm based on blind source separation(BSS) under the case that number of sources were unknown in advance and it was timevarying.The effectiveness of the proposed method was validated by simulation of time-invariant and time-varying numbers of source.Compared with other conventional methods,the proposed method has superior evaluation performances The proposed method can estimate m(the numbers of sensor) DOAs while other conventional methods estimate less than m DOAs.The R_(mse) of the proposed method in the case of low signal-to-noise ratio(SNR)(equal or lower than 30 dB) is smaller than 0.2 while R_(mse) of other conventional methods are greater than 0.8.展开更多
Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based ...Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.展开更多
A new method is presented to estimate two-dimensional （2-D） Direction-of-Arrival （DOA） angles of narrowband real-valued signals impinging on a L-shape Arrays（LA）. The basic idea of the proposed method is to incr...A new method is presented to estimate two-dimensional （2-D） Direction-of-Arrival （DOA） angles of narrowband real-valued signals impinging on a L-shape Arrays（LA）. The basic idea of the proposed method is to increase both the effective aperture size and the number of sensors by employing the conjugate invariance property of real-valued signals. Thus, the proposed method can provide a more precise DOA and detect more signals than the Cross-Correlation Matrix Method （CCMM）. Numerical simulation results are presented to support the theory.展开更多
在相干信号波达方向(direction of arrival,DOA)估计中,当阵列接收到的相干信号处于低信噪比时,DOA估计性能会大大降低。针对该问题,提出一种增强的时空平滑(enhanced spatio-temporal smoothing,ESTS)算法,在使用时空相关矩阵重构接收...在相干信号波达方向(direction of arrival,DOA)估计中,当阵列接收到的相干信号处于低信噪比时,DOA估计性能会大大降低。针对该问题,提出一种增强的时空平滑(enhanced spatio-temporal smoothing,ESTS)算法,在使用时空相关矩阵重构接收数据矩阵的时空平滑(spatio-temporal smoothing,STS)方法的基础上进行了改进。首先对子阵列时空相关矩阵进行平方预处理,然后通过充分利用子阵列时空相关矩阵的协方差和互协方差信息解相干,提高了相干信号的分辨率以及对噪声扰动的鲁棒性。理论分析和统计结果均表明,与其他空间平滑类解相干方法相比,该方法提高了在低信噪比、少快拍数、小角度分离情况下的相干信号DOA估计的去相关性能。展开更多
In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subar...In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix(CCM) of the subarrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays,and the unitary estimation of the signal parameters via rotational invariance techniques(ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio(SNR) environment.展开更多
In this paper,a low complexity ESPRIT algorithm based on power method and Orthogo- nal-triangular (QR) decomposition is presented for direction finding,which does not require a priori knowledge of source number and th...In this paper,a low complexity ESPRIT algorithm based on power method and Orthogo- nal-triangular (QR) decomposition is presented for direction finding,which does not require a priori knowledge of source number and the predetermined threshold (separates the signal and noise ei- gen-values).Firstly,according to the estimation of noise subspace obtained by the power method,a novel source number detection method without eigen-decomposition is proposed based on QR de- composition.Furthermore,the eigenvectors of signal subspace can be determined according to Q matrix and then the directions of signals could be computed by the ESPRIT algorithm.To determine the source number and subspace,the computation complexity of the proposed algorithm is approximated as (2log_2 n+2.67)M^3,where n is the power of covariance matrix and M is the number of array ele- ments.Compared with the Single Vector Decomposition (SVD) based algorithm,it has a substantial computational saving with the approximation performance.The simulation results demonstrate its effectiveness and robustness.展开更多
Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capa...Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.展开更多
Accurate estimation of the Direction-of-Arrival(DoA)of incident plane waves is essential for modern wireless communication,radar,sonar,and localization systems.Precise DoA information enables adaptive beamforming,spat...Accurate estimation of the Direction-of-Arrival(DoA)of incident plane waves is essential for modern wireless communication,radar,sonar,and localization systems.Precise DoA information enables adaptive beamforming,spatial filtering,and interference mitigation by steering antenna array beams toward desired sources while suppressing unwanted signals.Traditional one-dimensional Uniform Linear Arrays(ULAs)are limited to elevation angle estimation due to geometric constraints,typically within the range[0,π].To capture full spatial characteristics in environments with multipath and angular spread,joint estimation of both elevation and azimuth angles becomes necessary.However,existing 2D and 3D array geometries often entail increased hardware complexity and computational cost.This work proposes a novel and efficient framework for joint elevation and azimuth angle estimation using three spatially separated,parallel ULAs.The array configuration exploits spatial diversity and orthogonal projections to capture complete directional information with minimal structural overhead.A customized objective function based on the mean square error between measured and reconstructed array outputs is formulated to guide the estimation process.To solve the resulting non-convex optimization problem,three strategies are investigated:a global Genetic Algorithm(GA),a local Pattern Search(PS),and a hybrid GA-PS method that combines global exploration with local refinement.The proposed framework supports automatic pairing of elevation and azimuth angles,eliminating the need for manual post-processing.Extensive simulations validate the robustness,convergence,and accuracy of all three methods under varying signal-to-noise ratio conditions.Results confirm that the hybrid GA-PS approach achieves superior estimation performance and reduced computational complexity,making it well-suited for real-time and resource-constrained applications in next-generation sensing and communication systems.展开更多
In recent years,the proliferation of beamforming signals has made the electromagnetic environment more complex.Traditional spectrum sensing techniques mainly focus on the detection of omnidirectional signals and can n...In recent years,the proliferation of beamforming signals has made the electromagnetic environment more complex.Traditional spectrum sensing techniques mainly focus on the detection of omnidirectional signals and can no longer meet the needs of beamforming signals.Moreover,the next-generation spectrum sensing technologies must not only reliably detect the presence of beamforming signals but also accurately estimate the spatial information of these signals.This paper investigates the issue of Direction of Arrival(DOA)of non-cooperative Unmanned Aerial Vehicle(UAV)beamforming signals,where most of the prior information about non-cooperative transmitters,such as the transmission power and the communication time slots,is unknown.In such conditions,we consider two types of data models for UAV beamforming signals with different Signalto-Noise Ratios(SNRs).Based on these data models,we develop a UAV Beamforming signals Detection-DOA Network(BD-DOANet),comprising convolutional modules,a channel attention module,and residual modules.Simulation results show that BD-DOANet effectively captures the angle information of non-cooperative UAV beamforming signals for both ideal and non-ideal data.At higher SNR levels,the average error is below 0.5 and its mean squared error is below 0.2.Even at lower SNR levels,BD-DOANet shows superior performance of DOA estimation.展开更多
文摘The research on finding the arrival directions of speech signals by microphone arrny is proposed. We first analyze the uniform microphone array and give the design for microphone array applied in the hand-free speech recognition. Combining the traditional direction finding technique of MUltiple SIgnal Classification (MUSIC) with the focusing matrix method, we improve the resolving power of the microphone array for multiple speech sources.As one application of finding Direction of Arrival (DOA), a new microphone-array system for noise reduction is proposed. The new system is based on maximum likelihood estimate technique which reconstruct superimposed signals from different directions by using DOA information. The DOA information is got in terms of focusing MUSIC method which has been proven to have high performance than conventional MUSIC method on speaker localization[1].
基金supported by the National Natural Science Foundation of China(Grant Nos.61071163,61271327,and 61471191)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(Grant No.BCXJ14-08)+2 种基金the Funding of Innovation Program for Graduate Education of Jiangsu Province,China(Grant No.KYLX 0277)the Fundamental Research Funds for the Central Universities,China(Grant No.3082015NP2015504)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PADA),China
文摘This paper addresses the direction of arrival （DOA） estimation problem for the co-located multiple-input multiple- output （MIMO） radar with random arrays. The spatially distributed sparsity of the targets in the background makes com- pressive sensing （CS） desirable for DOA estimation. A spatial CS framework is presented, which links the DOA estimation problem to support recovery from a known over-complete dictionary. A modified statistical model is developed to ac- curately represent the intra-block correlation of the received signal. A structural sparsity Bayesian learning algorithm is proposed for the sparse recovery problem. The proposed algorithm, which exploits intra-signal correlation, is capable being applied to limited data support and low signal-to-noise ratio （SNR） scene. Furthermore, the proposed algorithm has less computation load compared to the classical Bayesian algorithm. Simulation results show that the proposed algorithm has a more accurate DOA estimation than the traditional multiple signal classification （MUSIC） algorithm and other CS recovery algorithms.
基金supported by the National Natural Science Foundation of China(62071476,62022091,61801488,61921001)the China Postdoctoral Science Foundation(2021T140788,2020M683728)+1 种基金the Science and Technology Innovation Program of Hunan Province(2020RC2041)the Research Program of National University of Defense Technology(ZK19-10,ZK20-33).
文摘Nonuniform linear arrays,such as coprime array and nested array,have received great attentions because of the increased degrees of freedom(DOFs)and weakened mutual coupling.In this paper,inspired by the existing coprime array,we propose a high-order extended coprime array(HoECA)for improved direction of arrival(DOA)estimation.We first derive the closed-form expressions for the range of consecutive lags.Then,by changing the inter-element spacing of a uniform linear array(ULA),three cases are proposed and discussed.It is indicated that the HoECA can obtain the largest number of consecutive lags when the spacing takes the maximum value.Finally,by comparing it with the other sparse arrays,the optimized HoECA enjoys a larger number of consecutive lags with mitigating mutual coupling.Simulation results are shown to evaluate the superiority of HoECA over the others in terms of DOF,mutual coupling leakage and estimation accuracy.
基金supported by the National Natural Science Foundation of China (60771042 60728101+2 种基金 60927002)the NSAF (10776003)the "111" Project (B07046)
文摘When the information of mutual coupling and shadowing effect of a conformal antenna array are unknown, the performance of direction of arrival （DOA） estimation will be seriously degraded by using some classical methods, such as the multiple signal classification （MUSIC） algorithm. Meanwhile it is difficult to measure or estimate the shadowing effect. The DOA estimation for a conformal uniform circular array （UCA） is studied. Firstly, the azimuthal angle is separated from all the unknown information by transforming the UCA from the element space to the mode space. Then the rank reduction （RARE） algorithm is applied in the estima- tion of the azimuthal angle. The π ambiguity existed in the RARE is solved by the beam forming. The main advantage of this method is that it does not need to measure the mutual coupling and the shadowing effect. Compared with the subarray method, it will not decrease the aperture of the array. Simulation results validate the advantages of the method.
基金This project was funded in part bythe U . S . Army
文摘A fiber Bragg grating (FBG) geophone and a surface seismic wave-based algorithm for detecting the direction of arrival (DOA) are described. The operational principle of FBG geophone is introduced and illustrated with systematic experimental data, demonstrating an improved FBG geophone with many advantages over the conventional geophones. An innovative, robust, and simple algorithm is developed for obtaining the bearing information on the seismic events, such as people walking, or vehicles moving. Such DOA estimate is based on the interactions and projections of surface-propagating seismic waves generated by the moving personnel or vehicles with a single tri-axial seismic sensor based on FBGs. Of particular interest is the case when the distance between the source of the seismic wave and the detector is less than or comparable to one wavelength (less than 100 m), corresponding to near-field detection, where an effective method of DOA finding lacks.
基金supported by the Chinese Natural Science Foundation 61401075Central University Business Fee ZYGX2015J106
文摘A theoretical relationship between the wavelet transform and the fast fourier transformation(FFT) methods in broadband wireless signal is proposed for solving the direction of arrivals(DOAs) estimation problem. This leads naturally to the derivation of minimum variance distortionless response(MVDR) algorithm, which combines the benefits of subspace methods with those of wavelet, and spatially smoothed versions are utilized which exhibits good performance against correlated signals. We test the method's performance by simulating and comparing the performance of proposed algorithm, FFT MVDR and MVDR with correlated signals, and an improved performance is obtained.
基金supported by the National Natural Science Foundation of China(61571149)the Natural Science Foundation of Heilongjiang Province(LH2020F017)+1 种基金the Initiation Fund for Postdoctoral Research in Heilongjiang Province(LBH-Q19098)the Heilongjiang Province Key Laboratory of High Accuracy Satellite Navigation and Marine Application Laboratory(HKL-2020-Y01).
文摘In order to resolve direction finding problems in the impulse noise,a direction of arrival(DOA)estimation method is proposed.The proposed DOA estimation method can restrain the impulse noise by using infinite norm exponential kernel covariance matrix and obtain excellent performance via the maximumlikelihood(ML)algorithm.In order to obtain the global optimal solutions of this method,a quantum electromagnetic field optimization(QEFO)algorithm is designed.In view of the QEFO algorithm,the proposed method can resolve the difficulties of DOA estimation in the impulse noise.Comparing with some traditional DOA estimation methods,the proposed DOA estimation method shows high superiority and robustness for determining the DOA of independent and coherent sources,which has been verified via the Monte-Carlo experiments of different schemes,especially in the case of snapshot deficiency,low generalized signal to noise ratio(GSNR)and strong impulse noise.Beyond that,the Cramer-Rao bound(CRB)of angle estimation in the impulse noise and the proof of the convergence of the QEFO algorithm are provided in this paper.
基金supported by the IOA Frontier Exploration Project (No.ZYTS202001)the Youth Innovation Promotion Association CAS。
文摘In underwater acoustic applications,the conventional cyclic direction of arrival algorithm faces challenges,including a low signal-to-noise ratio and high bandwidth when compared with modulated frequencies.In response to these issues,this paper introduces a novel,robust,and broadband cyclic beamforming algorithm.The proposed method substitutes the conventional cyclic covariance matrix with the variance of the cyclic covariance matrix as its primary feature.Assuming that the same frequency band shares a common steering vector,the new algorithm achieves superior detection performance for targets with specific modulation frequencies while suppressing interference signals and background noise.Experimental results demonstrate a significant enhancement in the directibity index by 81%and 181%when compared with the traditional Capon beamforming algorithm and the traditional extended wideband spectral cyclic MUSIC(EWSCM)algorithm,respectively.Moreover,the proposed algorithm substantially reduces computational complexity to 1/40th of that of the EWSCM algorithm,employing frequency band statistical averaging and covariance matrix variance.
基金Supported by the National High Technology and Devel-opment Program of China (2001AA631050)
文摘According to the characteristic of the echo of highfrequency ground wave radar（HF GWR）, which is one-dimensional narrow band signal, a virtual direction of arrival（DOA） matrix is constructed at first, then the DOA of target evaluation is achieved by the method of resolving equations for two-dimensional DOA matrix. And this method bases on the redundancy information of a linear two-row array of antennae. Both the simulation process and the treatment results of measured data （in the case of low SNR echoes and short data series） are given at the end of this paper. By comparing with GPS data of the targets, the validity and practical applicability of the method in this paper is verified.
文摘Linear antenna arrays(LAs)can be used to accurately predict the direction of arrival(DOAs)of various targets of interest in a given area.However,under certain conditions,LA suffers from the problem of ambiguities among the angles of targets,which may result inmisinterpretation of such targets.In order to cope up with such ambiguities,various techniques have been proposed.Unfortunately,none of them fully resolved such a problem because of rank deficiency and high computational cost.We aimed to resolve such a problem by proposing an algorithm using differential geometry.The proposed algorithm uses a specially designed doublet antenna array,which is made up of two individual linear arrays.Two angle observation models,ambiguous observation model(AOM)and estimated observation model(EOM),are derived for each individual array.The ambiguous set of angles is contained in the AOM,which is obtained from the corresponding array elements using differential geometry.The EOM for each array,on the other hand,contains estimated angles of all sources impinging signals on each array,as calculated by a direction-finding algorithm such as the genetic algorithm.The algorithm then contrasts the EOM of each array with its AOM,selecting the output of that array whose EOM has the minimum correlation with its corresponding AOM.In comparison to existing techniques,the proposed algorithm improves estimation accuracy and has greater precision in antenna aperture selection,resulting in improved resolution capabilities and the potential to be used more widely in practical scenarios.The simulation results using MATLAB authenticates the effectiveness of the proposed algorithm.
基金National Natural Science Foundation of China(61973037)National 173 Program Project(2019-JCJQ-ZD-324)。
文摘Uniform linear array(ULA)radars are widely used in the collision-avoidance radar systems of small unmanned aerial vehicles(UAVs).In practice,a ULA's multi-target direction of arrival(DOA)estimation performance suffers from significant performance degradation owing to the limited number of physical elements.To improve the underdetermined DOA estimation performance of a ULA radar mounted on a small UAV platform,we propose a nonuniform linear motion sampling underdetermined DOA estimation method.Using the motion of the UAV platform,the echo signal is sampled at different positions.Then,according to the concept of difference co-array,a virtual ULA with multiple array elements and a large aperture is synthesized to increase the degrees of freedom(DOFs).Through position analysis of the original and motion arrays,we propose a nonuniform linear motion sampling method based on ULA for determining the optimal DOFs.Under the condition of no increase in the aperture of the physical array,the proposed method obtains a high DOF with fewer sampling runs and greatly improves the underdetermined DOA estimation performance of ULA.The results of numerical simulations conducted herein verify the superior performance of the proposed method.
基金National Natural Science Foundation of China(No.51309116)the Foundation of Fujian Education Committee for Distinguished Young Scholars,China(No.JA14169)+2 种基金the Scientific Research Foundations of Jimei University,China(Nos.ZQ2013001,ZC2013012)Open Project of Artificial Intelligence Key Laboratory of Sichuan Province,China(No.2014RYJ03)Natural Science Foundation of Fujian Province,China(No.2016J01736)
文摘Aiming at source number determination and direction of arrival(DOA) estimation under the case of time-varying source number,a method of DOA estimation with an unknown number of sources was proposed.Firstly,an algorithm based on crossvalidation technique was introduced to determine the number of sources.Then dynamic DOAs of source were estimated using an algorithm based on blind source separation(BSS) under the case that number of sources were unknown in advance and it was timevarying.The effectiveness of the proposed method was validated by simulation of time-invariant and time-varying numbers of source.Compared with other conventional methods,the proposed method has superior evaluation performances The proposed method can estimate m(the numbers of sensor) DOAs while other conventional methods estimate less than m DOAs.The R_(mse) of the proposed method in the case of low signal-to-noise ratio(SNR)(equal or lower than 30 dB) is smaller than 0.2 while R_(mse) of other conventional methods are greater than 0.8.
基金supported by the National Natural Science Foundation of China(No.51279033).
文摘Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.
基金Supported by Program for New Century Excellent Talents in University
文摘A new method is presented to estimate two-dimensional （2-D） Direction-of-Arrival （DOA） angles of narrowband real-valued signals impinging on a L-shape Arrays（LA）. The basic idea of the proposed method is to increase both the effective aperture size and the number of sensors by employing the conjugate invariance property of real-valued signals. Thus, the proposed method can provide a more precise DOA and detect more signals than the Cross-Correlation Matrix Method （CCMM）. Numerical simulation results are presented to support the theory.
基金supported by the National Natural Science Foundation of China (NSFC) [grant number. 61871414]。
文摘In this paper, a novel direction of arrival(DOA) estimation algorithm using directional antennas in cylindrical conformal arrays(CCAs) is proposed. To eliminate the shadow effect, we divide the CCAs into several subarrays to obtain the complete output vector. Considering the anisotropic radiation pattern of a CCA, which cannot be separated from the manifold matrix, an improved interpolation method is investigated to transform the directional subarray into omnidirectional virtual nested arrays without non-orthogonal perturbation on the noise vector. Then, the cross-correlation matrix(CCM) of the subarrays is used to generate the consecutive co-arrays without redundant elements and eliminate the noise vector. Finally, the full-rank equivalent covariance matrix is constructed using the output of co-arrays,and the unitary estimation of the signal parameters via rotational invariance techniques(ESPRIT) is performed on the equivalent covariance matrix to estimate the DOAs with low computational complexity. Numerical simulations verify the superior performance of the proposed algorithm, especially under a low signal-to-noise ratio(SNR) environment.
基金Supported by the National Natural Science Foundation of China (No.60102005).
文摘In this paper,a low complexity ESPRIT algorithm based on power method and Orthogo- nal-triangular (QR) decomposition is presented for direction finding,which does not require a priori knowledge of source number and the predetermined threshold (separates the signal and noise ei- gen-values).Firstly,according to the estimation of noise subspace obtained by the power method,a novel source number detection method without eigen-decomposition is proposed based on QR de- composition.Furthermore,the eigenvectors of signal subspace can be determined according to Q matrix and then the directions of signals could be computed by the ESPRIT algorithm.To determine the source number and subspace,the computation complexity of the proposed algorithm is approximated as (2log_2 n+2.67)M^3,where n is the power of covariance matrix and M is the number of array ele- ments.Compared with the Single Vector Decomposition (SVD) based algorithm,it has a substantial computational saving with the approximation performance.The simulation results demonstrate its effectiveness and robustness.
基金the financial support from the National Key Research and Development Program of China(No.2023YFB3907001)the National Natural Science Foundation of China(Nos.U2233217,62371029)the UK Engineering and Physical Sciences Research Council(EPSRC),China(Nos.EP/M026981/1,EP/T021063/1 and EP/T024917/)。
文摘Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504)。
文摘Accurate estimation of the Direction-of-Arrival(DoA)of incident plane waves is essential for modern wireless communication,radar,sonar,and localization systems.Precise DoA information enables adaptive beamforming,spatial filtering,and interference mitigation by steering antenna array beams toward desired sources while suppressing unwanted signals.Traditional one-dimensional Uniform Linear Arrays(ULAs)are limited to elevation angle estimation due to geometric constraints,typically within the range[0,π].To capture full spatial characteristics in environments with multipath and angular spread,joint estimation of both elevation and azimuth angles becomes necessary.However,existing 2D and 3D array geometries often entail increased hardware complexity and computational cost.This work proposes a novel and efficient framework for joint elevation and azimuth angle estimation using three spatially separated,parallel ULAs.The array configuration exploits spatial diversity and orthogonal projections to capture complete directional information with minimal structural overhead.A customized objective function based on the mean square error between measured and reconstructed array outputs is formulated to guide the estimation process.To solve the resulting non-convex optimization problem,three strategies are investigated:a global Genetic Algorithm(GA),a local Pattern Search(PS),and a hybrid GA-PS method that combines global exploration with local refinement.The proposed framework supports automatic pairing of elevation and azimuth angles,eliminating the need for manual post-processing.Extensive simulations validate the robustness,convergence,and accuracy of all three methods under varying signal-to-noise ratio conditions.Results confirm that the hybrid GA-PS approach achieves superior estimation performance and reduced computational complexity,making it well-suited for real-time and resource-constrained applications in next-generation sensing and communication systems.
基金supported by the National Natural Science Foundation of China(Nos.U20B2038,62231027,62171462)。
文摘In recent years,the proliferation of beamforming signals has made the electromagnetic environment more complex.Traditional spectrum sensing techniques mainly focus on the detection of omnidirectional signals and can no longer meet the needs of beamforming signals.Moreover,the next-generation spectrum sensing technologies must not only reliably detect the presence of beamforming signals but also accurately estimate the spatial information of these signals.This paper investigates the issue of Direction of Arrival(DOA)of non-cooperative Unmanned Aerial Vehicle(UAV)beamforming signals,where most of the prior information about non-cooperative transmitters,such as the transmission power and the communication time slots,is unknown.In such conditions,we consider two types of data models for UAV beamforming signals with different Signalto-Noise Ratios(SNRs).Based on these data models,we develop a UAV Beamforming signals Detection-DOA Network(BD-DOANet),comprising convolutional modules,a channel attention module,and residual modules.Simulation results show that BD-DOANet effectively captures the angle information of non-cooperative UAV beamforming signals for both ideal and non-ideal data.At higher SNR levels,the average error is below 0.5 and its mean squared error is below 0.2.Even at lower SNR levels,BD-DOANet shows superior performance of DOA estimation.
