This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limit...This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.展开更多
On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC alg...On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.展开更多
This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail...This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail. It is also pointed out theoretically that this is equivalentto have increased the snapshot number and can make the DOA estimation better. Finally, somesimulating results to verify the theoretical analyses are presented.展开更多
In this paper,a two-dimensional(2 D)direction-of-arrival(DOA)estimation algorithm with increased degrees of freedom for two parallel linear arrays is presented.Being different from the conventional two-parallel linear...In this paper,a two-dimensional(2 D)direction-of-arrival(DOA)estimation algorithm with increased degrees of freedom for two parallel linear arrays is presented.Being different from the conventional two-parallel linear array,the proposed two-parallel linear array consists of two uniform linear arrays with non-equal inter-element spacing.Propagator method(PM)is used to obtain a special matrix which can be utilized to increase the virtual elements of one of uniform linear arrays.Then,the PM algorithm is used again to obtain automatically paired elevation and azimuth angles.The simulation results and complexity analysis show that the proposed method can increase the number of distinguishable signals and improve the estimation precision without increasing the computational complexity.展开更多
This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeit...This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.展开更多
The problem of two-dimensional direction of arrival(2D-DOA)estimation for uniform planar arrays(UPAs)is investigated by employing the reduced-dimensional(RD)polynomial root finding technique and 2D multiple signal cla...The problem of two-dimensional direction of arrival(2D-DOA)estimation for uniform planar arrays(UPAs)is investigated by employing the reduced-dimensional(RD)polynomial root finding technique and 2D multiple signal classification(2D-MUSIC)algorithm.Specifically,based on the relationship between the noise subspace and steering vectors,we first construct 2D root polynomial for 2D-DOA estimates and then prove that the 2D polynomial function has infinitely many solutions.In particular,we propose a computationally efficient algorithm,termed RD-ROOT-MUSIC algorithm,to obtain the true solutions corresponding to targets by RD technique,where the 2D root-finding problem is substituted by two one-dimensional(1D)root-finding operations.Finally,accurate 2DDOA estimates can be obtained by a sample pairing approach.In addition,numerical simulation results are given to corroborate the advantages of the proposed algorithm.展开更多
This paper introduces a method for solving DOA estimation ambiguity in ESPRIT algorithm with the conventional beamformer. With the help of it, for any space of two subarrays, the signal DOA in [-π/2 ,π/2] can be est...This paper introduces a method for solving DOA estimation ambiguity in ESPRIT algorithm with the conventional beamformer. With the help of it, for any space of two subarrays, the signal DOA in [-π/2 ,π/2] can be estimated effectively by using ESPRIT algorithm. Finally, some simulation results to verify the theoretical analyses are presented.展开更多
This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is es...This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.展开更多
A fast algorithm for DOA estimation without eigendecomposition is proposed. Unlike the available propagation method (PM), the proposed method need only use partial cross-correlation of array output data, and hence the...A fast algorithm for DOA estimation without eigendecomposition is proposed. Unlike the available propagation method (PM), the proposed method need only use partial cross-correlation of array output data, and hence the computational complexity is further reduced. Moreover, the proposed method is suitable for the case of spatially nonuniform colored noise. Simulation results show the performance of the proposed method is comparable to those of the existing PM method and the standard MUSIC method.展开更多
针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用...针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用大、运行时间长的问题,提出了一种基于极化MUSIC算法的四维参数联合估计FPGA实现架构。该架构包括信号协方差矩阵计算模块、Jacobi旋转模块、噪声子空间提取模块、两级空间谱搜索模块和极化参数计算模块。Jacobi旋转模块被拆分为多个可复用模块,并采用查找表模块生成旋转矩阵。一级空间谱搜索模块通过二维DOA搜索初步确定信源的角度信息。二级空间谱搜索模块根据一级搜索的角度结果确定二级搜索区域各点的极化信息,并计算该区域的四维空间谱,区域内最小值对应的四维参数信息即为最终估计的信源方向角、俯仰角、极化辅助角和极化相位角。仿真结果表明,与传统极化MUSIC算法的四维搜索算法相比,该架构避免了大量四维空间谱计算,同时保证了四维参数估计的精度,显著减少了运行时间和硬件资源消耗。展开更多
柱面共形阵由于其载体曲率的影响,导致在利用经典MUSIC(Multiple Signal Classifica-tion)算法进行DOA(Direction-of-Arrival)估计时,导向矢量与噪声子空间不正交,算法性能严重下降甚至失效。在考虑载体遮挡效应的同时,结合阵元的方向性...柱面共形阵由于其载体曲率的影响,导致在利用经典MUSIC(Multiple Signal Classifica-tion)算法进行DOA(Direction-of-Arrival)估计时,导向矢量与噪声子空间不正交,算法性能严重下降甚至失效。在考虑载体遮挡效应的同时,结合阵元的方向性,通过对导向矢量进行重构,解决了导向矢量与噪声子空间不正交这一问题。对比子阵分割MUSIC算法,进行了MonteCarlo仿真验证,分析了有向阵元MUSIC算法的估计性能。最后以多层圆柱阵为例对4个从不同方向入射的信源进行了DOA估计仿真验证。仿真结果表明:该方法具有分辨力高,估计精度高的优点,证明了该方法的有效性和高估计性能。展开更多
利用传统二维多重信号分类(multiple signal classification,MUSIC)算法进行二维波达方向(direction of arrival,DOA)估计时,往往带来巨大的运算量,限制了算法的实际应用。提出了一种能够大大降低二维DOA估计运算量的模值约束降维MUSIC...利用传统二维多重信号分类(multiple signal classification,MUSIC)算法进行二维波达方向(direction of arrival,DOA)估计时,往往带来巨大的运算量,限制了算法的实际应用。提出了一种能够大大降低二维DOA估计运算量的模值约束降维MUSIC算法,该算法将二维DOA估计问题转化为优化方程的求解问题,并采用模值约束法定义附加条件,使方向向量得到了较强约束,进而使求解结果更加接近最优解。理论分析和仿真实验表明,本文算法所需运算量较低,且角度估计的成功率与精确度较高。展开更多
文摘This paper gives a MUSIC signal DOA estimation algorithm based on the modified high-order cumulant matrix which is constructed by the recieved data and their conjugate rearrangements. When the snapshot number is limited, this algorithm can improve the signal DOA estimation performances obviously, and its computational complexity scarcely increases. Finally, some simulation results to verify the theoretical analyses are presented.
文摘On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.
文摘This paper presents a modified Root-MUSIC algorithm by which the signal DOA estimation performance can be improved when the snapshot number is limited. The operation principlesof this algorithm are described in detail. It is also pointed out theoretically that this is equivalentto have increased the snapshot number and can make the DOA estimation better. Finally, somesimulating results to verify the theoretical analyses are presented.
基金supported by the National Natural Science Foundation of China(51877015,U1831117)the Cooperation Agreement Foundation by the Department of Science and Technology of Guizhou Province of China(LH[2017]7320,LH[2017]7321,[2015]7249)+2 种基金the Innovation Group Major Research Program Funded by Guizhou Provincial Education Department(KY[2016]051)the Foundation of Top-notch Talents by Education Department of Guizhou Province of China(KY[2018]075)PhD Research Startup Foundation of Tongren University(trxy DH1710)。
文摘In this paper,a two-dimensional(2 D)direction-of-arrival(DOA)estimation algorithm with increased degrees of freedom for two parallel linear arrays is presented.Being different from the conventional two-parallel linear array,the proposed two-parallel linear array consists of two uniform linear arrays with non-equal inter-element spacing.Propagator method(PM)is used to obtain a special matrix which can be utilized to increase the virtual elements of one of uniform linear arrays.Then,the PM algorithm is used again to obtain automatically paired elevation and azimuth angles.The simulation results and complexity analysis show that the proposed method can increase the number of distinguishable signals and improve the estimation precision without increasing the computational complexity.
文摘This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)radar.The existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low accuracy.We aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix approximation.Specifically,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its subspaces.That is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation error.Relying on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation accuracy.Furthermore,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation.As validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time complexity.Thus,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.
基金supported by the National Natural Science Foundation of China(Nos.61631020,61971218,61601167,61371169)。
文摘The problem of two-dimensional direction of arrival(2D-DOA)estimation for uniform planar arrays(UPAs)is investigated by employing the reduced-dimensional(RD)polynomial root finding technique and 2D multiple signal classification(2D-MUSIC)algorithm.Specifically,based on the relationship between the noise subspace and steering vectors,we first construct 2D root polynomial for 2D-DOA estimates and then prove that the 2D polynomial function has infinitely many solutions.In particular,we propose a computationally efficient algorithm,termed RD-ROOT-MUSIC algorithm,to obtain the true solutions corresponding to targets by RD technique,where the 2D root-finding problem is substituted by two one-dimensional(1D)root-finding operations.Finally,accurate 2DDOA estimates can be obtained by a sample pairing approach.In addition,numerical simulation results are given to corroborate the advantages of the proposed algorithm.
文摘This paper introduces a method for solving DOA estimation ambiguity in ESPRIT algorithm with the conventional beamformer. With the help of it, for any space of two subarrays, the signal DOA in [-π/2 ,π/2] can be estimated effectively by using ESPRIT algorithm. Finally, some simulation results to verify the theoretical analyses are presented.
文摘This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.
文摘A fast algorithm for DOA estimation without eigendecomposition is proposed. Unlike the available propagation method (PM), the proposed method need only use partial cross-correlation of array output data, and hence the computational complexity is further reduced. Moreover, the proposed method is suitable for the case of spatially nonuniform colored noise. Simulation results show the performance of the proposed method is comparable to those of the existing PM method and the standard MUSIC method.
文摘针对在现场可编程门阵列(Field Programmable Gate Array,FPGA)上实现基于极化敏感阵列的多重信号分类(Multiple Signal Classification,MUSIC)算法进行二维波达方向(Direction of Arrival,DOA)和二维极化参数联合估计时,硬件资源占用大、运行时间长的问题,提出了一种基于极化MUSIC算法的四维参数联合估计FPGA实现架构。该架构包括信号协方差矩阵计算模块、Jacobi旋转模块、噪声子空间提取模块、两级空间谱搜索模块和极化参数计算模块。Jacobi旋转模块被拆分为多个可复用模块,并采用查找表模块生成旋转矩阵。一级空间谱搜索模块通过二维DOA搜索初步确定信源的角度信息。二级空间谱搜索模块根据一级搜索的角度结果确定二级搜索区域各点的极化信息,并计算该区域的四维空间谱,区域内最小值对应的四维参数信息即为最终估计的信源方向角、俯仰角、极化辅助角和极化相位角。仿真结果表明,与传统极化MUSIC算法的四维搜索算法相比,该架构避免了大量四维空间谱计算,同时保证了四维参数估计的精度,显著减少了运行时间和硬件资源消耗。
文摘柱面共形阵由于其载体曲率的影响,导致在利用经典MUSIC(Multiple Signal Classifica-tion)算法进行DOA(Direction-of-Arrival)估计时,导向矢量与噪声子空间不正交,算法性能严重下降甚至失效。在考虑载体遮挡效应的同时,结合阵元的方向性,通过对导向矢量进行重构,解决了导向矢量与噪声子空间不正交这一问题。对比子阵分割MUSIC算法,进行了MonteCarlo仿真验证,分析了有向阵元MUSIC算法的估计性能。最后以多层圆柱阵为例对4个从不同方向入射的信源进行了DOA估计仿真验证。仿真结果表明:该方法具有分辨力高,估计精度高的优点,证明了该方法的有效性和高估计性能。
文摘利用传统二维多重信号分类(multiple signal classification,MUSIC)算法进行二维波达方向(direction of arrival,DOA)估计时,往往带来巨大的运算量,限制了算法的实际应用。提出了一种能够大大降低二维DOA估计运算量的模值约束降维MUSIC算法,该算法将二维DOA估计问题转化为优化方程的求解问题,并采用模值约束法定义附加条件,使方向向量得到了较强约束,进而使求解结果更加接近最优解。理论分析和仿真实验表明,本文算法所需运算量较低,且角度估计的成功率与精确度较高。
