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
To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a de...To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.展开更多
With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direc...With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.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
A direction-of-arrival （DOA） estimation algorithm based on direct data domain （D3） approach is presented. This method can accuracy estimate DOA using one snapshot modified data, called the temporal and spatial two...A direction-of-arrival （DOA） estimation algorithm based on direct data domain （D3） approach is presented. This method can accuracy estimate DOA using one snapshot modified data, called the temporal and spatial two-dimensional vector reconstruction （TSR） method. The key idea is to apply the D3 approach which can extract the signal of given frequency but null out other frequency signals in temporal domain. Then the spatial vector reconstruction processing is used to estimate the angle of the spatial coherent signal source based on extract signal data. Compared with the common temporal and spatial processing approach, the TSR method has a lower computational load, higher real-time performance, robustness and angular accuracy of DOA. The proposed algorithm can be directly applied to the phased array radar of coherent pulses. Simulation results demonstrate the performance of the proposed technique.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
A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival（DOA） estimation of second-order fully noncircular source signals, using a uniform linear array（ULA）. This algorithm ...A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival（DOA） estimation of second-order fully noncircular source signals, using a uniform linear array（ULA）. This algorithm inherits all merits of its spectralsearching counterpart except for the applicability to arbitrary array geometry, while reducing considerably the computation cost.Simulation results show that the proposed algorithm outperforms the previously developed closed-form second-order noncircular ESPRIT method, in terms of processing capacity and DOA estimation accuracy, especially in the presence of spatially colored noise.展开更多
A new direction-of-arrival （DOA） estimation algorithm for wideband sources is introduced, The new method obtains the output of the virtual arrays in the signal bandwidth using cubic spline function interpolation tec...A new direction-of-arrival （DOA） estimation algorithm for wideband sources is introduced, The new method obtains the output of the virtual arrays in the signal bandwidth using cubic spline function interpolation techniques. The narrowband high- resolution algorithm is then used to get the DOA estimation. This technique does not require any preliminary knowledge of DOA angles. Simulation results demonstrate the effectiveness of the method.展开更多
A novel identification method for point source,coherently distributed（CD） source and incoherently distributed（ICD） source is proposed.The differences among the point source,CD source and ICD source are studied.Acc...A novel identification method for point source,coherently distributed（CD） source and incoherently distributed（ICD） source is proposed.The differences among the point source,CD source and ICD source are studied.According to the different characters of covariance matrix and general steering vector of the array received source,a second order blind identification method is used to separate the sources,the mixing matrix could be obtained.From the mixing matrix,the type of the source is identified by using an amplitude criterion.And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.Computer simulations validate the efficiency of the method.展开更多
针对互耦效应和脉冲噪声并存环境下的波达方向(direction of arrival,DOA)估计问题,提出一种结合M估计与稀疏重构的算法。首先,为了消除互耦效应的影响,依据互耦矩阵的托普利兹结构进行恒等变形,得到了不含未知互耦系数的字典。随后,为...针对互耦效应和脉冲噪声并存环境下的波达方向(direction of arrival,DOA)估计问题,提出一种结合M估计与稀疏重构的算法。首先,为了消除互耦效应的影响,依据互耦矩阵的托普利兹结构进行恒等变形,得到了不含未知互耦系数的字典。随后,为了使算法能适应高斯噪声和不同强度的脉冲噪声,将位置得分函数表示为高斯位置得分函数和一系列非线性函数的线性组合,利用噪声样本估计线性组合系数从而建立损失函数。最后,采用迭代硬阈值算法进行稀疏重构,并通过改进信号更新策略提高正确收敛的概率。仿真结果表明,所提算法能有效抑制互耦效应和脉冲(高斯)噪声的干扰,同时相较已有算法在低信噪比、强脉冲特性下的性能有显著提升。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 order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on it...In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.展开更多
This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of th...This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of the DCN are the major challenge.In our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization errors.We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation.Then,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate.Also,the feasibility of the proposed deep estimator is analyzed in detail in this paper.Once the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and accurately.Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.展开更多
由于传统的欧式空间方法无法有效反映协方差矩阵之间的差异,而导致信息损失,为了解决这一问题,提出了一种基于詹森-布雷格曼洛格德特散度(Jensen-Bregman LogDet divergence)的阵列波达方向(Direction of Arrival,DOA)估计方法,将目标...由于传统的欧式空间方法无法有效反映协方差矩阵之间的差异,而导致信息损失,为了解决这一问题,提出了一种基于詹森-布雷格曼洛格德特散度(Jensen-Bregman LogDet divergence)的阵列波达方向(Direction of Arrival,DOA)估计方法,将目标方位估计问题转化为矩阵流形上两点间的几何距离问题,揭示了方位估计与黎曼空间矩阵流形的映射规律,从而得到了几何距离最小值处对应的角度即为目标入射角度的结论,并通过构建两个强鲁棒性的矩阵流形,完成了矩阵信息几何DOA估计理论模型的建立。通过模拟仿真与实测数据对所新方法进行了验证。验证结果表明:与现有的最小方差无失真响应算法和多信号分类算法相比,新方法在低信噪比环境下拥有更好的估计精度;新方法的应用具有一定的实际意义和应用前景,可以为海洋防御及民用领域中的水下目标方位估计等提供坚实的技术支持。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
The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the nar...The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the narrowband case is considered.Based on the small perturbation assumption, this paper proposes an augmentation scheme so as to estimate DOA and perturbation parameters. The E-step and M-step of the SAGE algorithm in this case are derived. Then, the algorithm is extended to the wideband case. The wideband SAGE algorithm is derived in frequency domain by jointing all frequency bins. Simulation results show that the algorithm achieves good convergence and high parameter estimation precision.展开更多
针对现有的波达方向(direction of arrival,DOA)估计方法在低信噪比、小快拍、多信源条件下估计精度较低的问题,提出一种基于并行坐标下降算法的DOA估计方法.首先,对空域等角度均匀划分,构造超完备冗余字典;其次,采用并行坐标下降算法...针对现有的波达方向(direction of arrival,DOA)估计方法在低信噪比、小快拍、多信源条件下估计精度较低的问题,提出一种基于并行坐标下降算法的DOA估计方法.首先,对空域等角度均匀划分,构造超完备冗余字典;其次,采用并行坐标下降算法的思想对稀疏信号进行重构,得到信号在空域的稀疏系数矩阵;最后,将稀疏矩阵行向量的l2-范数映射到空域网格上,得到准确的DOA估计值.仿真实验结果表明:在低信噪比、小快拍、多信源条件下,该方法优于子空间类算法、贪婪类算法以及凸优化类算法,具有更低的均方根误差(RMSE)、更高的DOA估计精度和运行效率.展开更多
This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time...This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.展开更多
Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the constructi...Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.展开更多
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supported by the National Natural Science Foundation of China(62301598).
文摘To tackle the challenges of intractable parameter tun-ing,significant computational expenditure and imprecise model-driven sparse-based direction of arrival(DOA)estimation with array error(AE),this paper proposes a deep unfolded amplitude-phase error self-calibration network.Firstly,a sparse-based DOA model with an array convex error restriction is established,which gets resolved via an alternating iterative minimization(AIM)algo-rithm.The algorithm is then unrolled to a deep network known as AE-AIM Network(AE-AIM-Net),where all parameters are opti-mized through multi-task learning using the constructed com-plete dataset.The results of the simulation and theoretical analy-sis suggest that the proposed unfolded network achieves lower computational costs compared to typical sparse recovery meth-ods.Furthermore,it maintains excellent estimation performance even in the presence of array magnitude-phase errors.
基金supported by the National Basic Research Program of China。
文摘With the extensive application of large-scale array antennas,the increasing number of array elements leads to the increasing dimension of received signals,making it difficult to meet the real-time requirement of direction of arrival(DOA)estimation due to the computational complexity of algorithms.Traditional subspace algorithms require estimation of the covariance matrix,which has high computational complexity and is prone to producing spurious peaks.In order to reduce the computational complexity of DOA estimation algorithms and improve their estimation accuracy under large array elements,this paper proposes a DOA estimation method based on Krylov subspace and weighted l_(1)-norm.The method uses the multistage Wiener filter(MSWF)iteration to solve the basis of the Krylov subspace as an estimate of the signal subspace,further uses the measurement matrix to reduce the dimensionality of the signal subspace observation,constructs a weighted matrix,and combines the sparse reconstruction to establish a convex optimization function based on the residual sum of squares and weighted l_(1)-norm to solve the target DOA.Simulation results show that the proposed method has high resolution under large array conditions,effectively suppresses spurious peaks,reduces computational complexity,and has good robustness for low signal to noise ratio(SNR)environment.
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.
文摘A direction-of-arrival （DOA） estimation algorithm based on direct data domain （D3） approach is presented. This method can accuracy estimate DOA using one snapshot modified data, called the temporal and spatial two-dimensional vector reconstruction （TSR） method. The key idea is to apply the D3 approach which can extract the signal of given frequency but null out other frequency signals in temporal domain. Then the spatial vector reconstruction processing is used to estimate the angle of the spatial coherent signal source based on extract signal data. Compared with the common temporal and spatial processing approach, the TSR method has a lower computational load, higher real-time performance, robustness and angular accuracy of DOA. The proposed algorithm can be directly applied to the phased array radar of coherent pulses. Simulation results demonstrate the performance of the proposed technique.
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(617020986170209961331019)
文摘A polynomial-rooting based fourth-order cumulant algorithm is presented for direction-of-arrival（DOA） estimation of second-order fully noncircular source signals, using a uniform linear array（ULA）. This algorithm inherits all merits of its spectralsearching counterpart except for the applicability to arbitrary array geometry, while reducing considerably the computation cost.Simulation results show that the proposed algorithm outperforms the previously developed closed-form second-order noncircular ESPRIT method, in terms of processing capacity and DOA estimation accuracy, especially in the presence of spatially colored noise.
文摘A new direction-of-arrival （DOA） estimation algorithm for wideband sources is introduced, The new method obtains the output of the virtual arrays in the signal bandwidth using cubic spline function interpolation techniques. The narrowband high- resolution algorithm is then used to get the DOA estimation. This technique does not require any preliminary knowledge of DOA angles. Simulation results demonstrate the effectiveness of the method.
文摘A novel identification method for point source,coherently distributed（CD） source and incoherently distributed（ICD） source is proposed.The differences among the point source,CD source and ICD source are studied.According to the different characters of covariance matrix and general steering vector of the array received source,a second order blind identification method is used to separate the sources,the mixing matrix could be obtained.From the mixing matrix,the type of the source is identified by using an amplitude criterion.And the direction of arrival for the array received source is estimated by using the matching pursuit algorithm from the vectors of the mixing matrix.Computer simulations validate the efficiency of the method.
文摘针对互耦效应和脉冲噪声并存环境下的波达方向(direction of arrival,DOA)估计问题,提出一种结合M估计与稀疏重构的算法。首先,为了消除互耦效应的影响,依据互耦矩阵的托普利兹结构进行恒等变形,得到了不含未知互耦系数的字典。随后,为了使算法能适应高斯噪声和不同强度的脉冲噪声,将位置得分函数表示为高斯位置得分函数和一系列非线性函数的线性组合,利用噪声样本估计线性组合系数从而建立损失函数。最后,采用迭代硬阈值算法进行稀疏重构,并通过改进信号更新策略提高正确收敛的概率。仿真结果表明,所提算法能有效抑制互耦效应和脉冲(高斯)噪声的干扰,同时相较已有算法在低信噪比、强脉冲特性下的性能有显著提升。
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(61671352)the open foundation of Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education(Guilin University of Electronic Technology)(CRKL160206)Xi’an University of Science and Technology Doctor(after)Start Gold Project(2017QDJ018)
文摘In order to solve the problem of coherent signal subspace method(CSSM) depending on the estimated accuracy of signal subspace, a new direction of arrival(DOA) estimation method of wideband source, which is based on iterative adaptive spectral reconstruction, is proposed. Firstly, the wideband signals are divided into several narrowband signals of different frequency bins by discrete Fourier transformation(DFT). Then, the signal matched power spectrum in referenced frequency bins is computed, which can form the initial covariance matrix. Finally, the linear restrained minimum variance spectral(Capon spectral) of signals in other frequency bins are reconstructed using sequential iterative means, so the DOA can be estimated by the locations of spectral peaks. Theoretical analysis and simulation results show the proposed method based on the iterative spectral reconstruction for the covariance matrices of all sub-bands can avoid the problem of determining the signal subspace accurately with the coherent signal subspace method under the conditions of small samples and low signal to noise ratio(SNR), and it can also realize full dimensional focusing of different sub-band data, which can be applied to coherent sources and can significantly improve the accuracy of DOA estimation.
文摘This paper develops a deep estimator framework of deep convolution networks(DCNs)for super-resolution direction of arrival(DOA)estimation.In addition to the scenario of correlated signals,the quantization errors of the DCN are the major challenge.In our deep estimator framework,one DCN is used for spectrum estimation with quantization errors,and the remaining two DCNs are used to estimate quantization errors.We propose training our estimator using the spatial sampled covariance matrix directly as our deep estimator’s input without any feature extraction operation.Then,we reconstruct the original spatial spectrum from the spectrum estimate and quantization errors estimate.Also,the feasibility of the proposed deep estimator is analyzed in detail in this paper.Once the deep estimator is appropriately trained,it can recover the correlated signals’spatial spectrum fast and accurately.Simulation results show that our estimator performs well in both resolution and estimation error compared with the state-of-the-art algorithms.
文摘由于传统的欧式空间方法无法有效反映协方差矩阵之间的差异,而导致信息损失,为了解决这一问题,提出了一种基于詹森-布雷格曼洛格德特散度(Jensen-Bregman LogDet divergence)的阵列波达方向(Direction of Arrival,DOA)估计方法,将目标方位估计问题转化为矩阵流形上两点间的几何距离问题,揭示了方位估计与黎曼空间矩阵流形的映射规律,从而得到了几何距离最小值处对应的角度即为目标入射角度的结论,并通过构建两个强鲁棒性的矩阵流形,完成了矩阵信息几何DOA估计理论模型的建立。通过模拟仿真与实测数据对所新方法进行了验证。验证结果表明:与现有的最小方差无失真响应算法和多信号分类算法相比,新方法在低信噪比环境下拥有更好的估计精度;新方法的应用具有一定的实际意义和应用前景,可以为海洋防御及民用领域中的水下目标方位估计等提供坚实的技术支持。
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.
文摘The direction of arrival(DOA) estimation problem in the presence of sensor location errors is studied and an algorithm based on space alternating generalized expectation-maximization(SAGE) is presented. First, the narrowband case is considered.Based on the small perturbation assumption, this paper proposes an augmentation scheme so as to estimate DOA and perturbation parameters. The E-step and M-step of the SAGE algorithm in this case are derived. Then, the algorithm is extended to the wideband case. The wideband SAGE algorithm is derived in frequency domain by jointing all frequency bins. Simulation results show that the algorithm achieves good convergence and high parameter estimation precision.
文摘针对现有的波达方向(direction of arrival,DOA)估计方法在低信噪比、小快拍、多信源条件下估计精度较低的问题,提出一种基于并行坐标下降算法的DOA估计方法.首先,对空域等角度均匀划分,构造超完备冗余字典;其次,采用并行坐标下降算法的思想对稀疏信号进行重构,得到信号在空域的稀疏系数矩阵;最后,将稀疏矩阵行向量的l2-范数映射到空域网格上,得到准确的DOA估计值.仿真实验结果表明:在低信噪比、小快拍、多信源条件下,该方法优于子空间类算法、贪婪类算法以及凸优化类算法,具有更低的均方根误差(RMSE)、更高的DOA估计精度和运行效率.
基金supported by the National Natural Science Foundation of China(61072120)
文摘This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.
基金supported by the National Natural Science Foundation of China(6117119761371045+2 种基金61201307)the Shandong Provincial Promotive Research Fund for Excellent Young and Middle-aged Scientists(BS2010DX001)the Shandong Provincial Natural Science Foundation (ZR2011FM005)
文摘Compressive sensing（CS） has emerged as a novel sampling framework which enables sparse signal acquisition and reconstruction with fewer measurements below the Nyquist rate.An important issue for CS is the construction of measurement matrix or sensing matrix.A new deterministic sensing matrix,named as OOC-B,is proposed by exploiting optical orthogonal codes（OOCs）,Bernoulli matrix and Singer structure,which has the entries of 0,＋1 and-1 before normalization.We have proven that the designed deterministic matrix is asymptotically optimal.In addition,the proposed deterministic sensing matrix is applied to direction of arrival（DOA） estimation of narrowband signals by CS arrays（CSA）processing and CS recovery.Theoretical analysis and simulation results show that the proposed sensing matrix has good performance for DOA estimation.It is very effective for simplifying hardware structure and decreasing computational complexity in DOA estimation by CSA processing.Besides,lower root mean square error（RMSE） and bias are obtained in DOA estimation by CS recovery.
