利用有效相位模式计算任意阵列MUSIC谱的快速算法

针对任意阵列MUSIC谱的快速计算问题,提出了一种利用模式空间中有效相位模式(EPM)计算MUSIC谱的快速算法。利用空间谱计算时不同方向各阵元的空间相移因子构成一组新的方向向量,由于各方向向量与噪声子空间中特征向量的乘法计算具有循环卷积特性,从而可以利用DFT实现MUSIC谱函数的计算。新的方向向量可以等效为对相应的虚拟连续圆阵采样的结果,根据圆阵能激发的有效相位模式数远小于MUSIC谱的点数的事实,利用DFT计算MUSIC谱时只需要使用有效的相位模式参与计算,从而使计算量大幅度减小。根据方向向量的过采样特性,通过对方向向量进行抽取,利用抽取后的序列计算有效相位模式,进一步较小了计算量。通过仿真实验验证了提出算法的有效性。      

圆阵二维MUSIC谱快速计算方法研究

针对二维波达方向估计时MUSIC谱的快速计算问题,研究了均匀圆阵变换到虚拟线阵的MUSIC算法(UCA-ULA-MUSIC)、流形分离MUSIC算法(MS-MUSIC)、傅立叶域线性求根MUSIC算法(FD-Line-Search-MUSIC)、基于FFT的 元均匀圆阵MUSIC算法( -UCA-FFT-MUSIC)与基于FFT的任意圆阵MUSIC算法(ACA-FFT-MUSIC)。对各种算法快速计算二维MUSIC谱的实现步骤进行了总结。在此基础上,给出了各算法计算二维MUSIC谱的计算复杂度表达式,并以传统方法为参考,对比了各种快速算法相对于传统方法的计算复杂度比值;同时,针对不同的阵列形式,对适用的快速算法的测向性能进行了仿真对比。根据分析和对比的结果,指出MS-MUSIC算法与ACA-FFT-MUSIC算法具有更高的工程应用价值,由具体的情况单独或分频段联合使用MS-MUSIC算法与ACA-FFT-MUSIC算法,可以使测向系统较好的兼顾测向性能与时效性。      

一种基于波束空间的单次快拍MUSIC算法

针对MUSIC算法在工程应用中存在的计算量大、快拍需求数多等问题,详细研究了一种单次快拍MUSIC算法。针对这种算法存在的分辨力差、估计偏差大等缺陷,提出了一种新的基于波束空间的单次快拍MUSIC算法,该方法首先利用单次快拍来估计阵列数据的协方差矩阵,再将常规波束形成方法和MUSIC超分辨方法相结合,实现对空间谱的估计。仿真结果表明,这种新方法提高了分辨力,降低了估计偏差,进一步减少了运算量。     

基于均圆阵的扩展循环 MUSIC 算法简

将循环平稳理论引入到均匀圆阵的波达方向估计中,提出了一种基于圆阵的扩展循环MUSIC算法。同时利用信号的循环相关阵和循环共轭相关阵的信息,有效抑制了同频带内干扰和噪声的影响,实现了感兴趣信号与干扰信号的有效分离。仿真实验结果表明,所提算法具有较高的波达方向估计精度和较好的多信号分辨能力,且突破了经典MUSIC算法关于信源数不能超过阵元数的限制。     

均匀圆阵二维波达角估计的Cramer-Rao界

均匀圆阵(UCA)是一种应用广泛的具有二位波达角估计能力的平面阵列。为了从理论上分析不同阵列参数下到达波方位角(AOA)、仰角估计精度,推导了均匀圆阵二维波达角估计的性能界,以此为基础分析了阵列孔径、阵元个数、快拍数以及来波仰角高低与到达角估计精度的关系,并通过对UCA-MUSIC算法计算机仿真验证了推导结果的正确性。研究结果为波达角估计类算法提供了可供参考的性能下界,圆阵设计时也不再需要大量的Monte Carlo仿真试验确定阵列参数,可直接从估计精度表达式中获得。     

Spatially Close Signals Separation via Array Aperture Expansions and Spatial Spectrum Averaging

A resolution enhancement method for estimating the direction‐of‐arrival (DOA) of signals is presented. The proposed method is by virtually expanding a real array into virtual arrays and then averaging the spatial spectrum of the virtual arrays, each of which has a different aperture size. Superior DOA resolutions are shown in comparison with the standard algorithm, MUltiple SIgnal Classification (MUSIC), for incoherent signals incident on a uniform circular array.     

共形阵列天线MUSIC算法性能分析

该文在建立了三维共形天线阵列流形的数学模型基础上,将经典高分辨波达方向(DOA)估计方法多重信号分类(MUSIC)算法移植到共形阵列天线中;详细分析推导了MUSIC算法在共形阵列天线 (锥面阵列、柱面阵列、球面阵列)中的估计方差、克拉美-罗界(CRB);通过计算机仿真试验对比MUSIC算法在面阵(均匀线阵、均匀圆阵)与共形阵列中的性能函数以及估计方差,给出了MUSIC 算法在不同阵列形式中DOA估计性能的评估与比较,仿真结果显示MUSIC算法在不同阵列形式中的估计性能均随阵元个数以及信噪比的增加而变好,验证了理论分析的正确性。     

A novel DOA estimation algorithm of cyclostationary signal based on UCA in impulsive noise

Co-channel interference is a common existence in communication system, which will result in the deterioration for directions-of-arrival (DOAs) estimation. To solve this problem, a fractional lower order cyclic MUSIC method suitable for uniform circular array (UCA) is presented. The proposed method can eliminate both impulsive-noise and interference in the same frequency band, and it also performs well in Gaussian noise. Especially, the algorithm can be applied to either narrow or broad-band signal models. Simulation results strongly verify the high performance of this method.     

Fast music spectrum peak search VIA Metropolis-Hastings sampler

A fast MUltiple Signal Classification （MUSIC） spectrum peak search algorithm is devised, which regards the power of the MUSIC spectrum function as target distribution up to a constant of proportionality, and uses Metropolis-Hastings （MH） sampler, one of the most popular Markov Chain Monte Carlo （MCMC） techniques, to sample from it. The proposed method reduces greatly the tremendous computation and storage costs in conventional MUSIC techniques i.e., about two and four orders of magnitude in computation and storage costs under the conditions of the experiment in the paper respectively.     

MODE SPACE SMOOTHING ALGORITHM TO ESTIMATE DOA OF COHERENT SIGNALS IMPINGING ON UNIFORM CIRCULAR ARRAY

A novel method to estimate DOA of coherent signals impinging on a uniform circular array( UCA) is presented in this paper. A virtual uniform linear array (VULA) is first derived by using spatial DFT technique, transforming the UCA from element space to phase mode space to obtain the properties of ordinary ULA, and then the well known spatial smoothing technique is applied to the VULA so that the lost rank of covariance matrix due to signal coherence can be retrieved. This method makes it feasible to use the simple MUSIC algorithm to estimate DOA of coherent signals impinging on a UCA without heavy computation burden. Simulation results strongly verify the effectiveness of the algorithm.     

Statistical Performance of MUSIC-Like Algorithms in Resolving Noncircular Sources

This paper addresses the resolution of the conventional and noncircular MUSIC algorithms for arbitrary circular and noncircular second-order distributions of two uncorrelated closely spaced transmitters observed by an arbitrary array. An explicit closed-form expression of the mean null spectrum of the conventional and noncircular MUSIC algorithms is derived using an analysis based on perturbations of the noise projector instead of those of the eigenvectors. Based on these results, theoretical and approximate interpretable closed-form expressions of the threshold array signal-to-noise ratios (ASNR) at which these two algorithms are able to resolve two closely spaced transmitters along the Cox and the Sharman and Durrani criteria are given. It is proved that the threshold ASNRs given by the conventional MUSIC algorithm do not depend on the distribution of the sources including their noncircularity, in contrast to the noncircular MUSIC algorithm for which they are very sensitive to the noncircularity phase separation of the sources. This threshold ASNR given by the noncircular MUSIC algorithm is proven to be comfortably lower than that given by the conventional MUSIC algorithm except for weak phase separations of the sources for which the resolving powers of these two algorithms are very close. Finally, these results are analyzed through several illustrations and Monte Carlo simulations.      

柱面共形阵列高性能低复杂度DOA估计算法

针对柱面共形阵列的波达方向(DOA)估计问题,从信号子空间的角度分析了在阵元遮挡下应用多重信号分类(MUSIC)算法的性能缺陷。在此基础上提出通过偏置常数的方法克服经典MUSIC算法的阵元遮挡问题。进一步提出一种基于数据自适应子阵分割的快速DOA估计算法,该方法先利用稀疏采样的偏置MUSIC算法进行DOA预估,依此确定所需要的子阵及二维搜索区域,确定MUSIC算法的搜索范围,进而得到高精确度的DOA估计。利用子阵分割的方法进行DOA估计,避免了经典MUSIC算法因阵元遮挡导致运算量大、精确度低等问题。仿真结果表明,该方法能大幅度降低运算复杂度,同时提高DOA估计精确度。     

MUSIC测向算法的入射方向确定方法

MUSIC(MUltiple SIgnal Classification)等谱峰搜索类算法的计算量主要集中于谱峰搜索过程,加大搜索步长虽可减小计算量,但会引起空间谱的畸变从而造成测向误差。先采用大步长搜索确定谱峰大约位置后再用小步长搜索可以减小测向误差,且增加的计算量较小,但其实时性不佳。文中提出了基于重心估计的入射方向确定方法,利用现有谱峰搜索数据进行后处理,当搜索步长远大于MUSIC测向算法本身的均方根误差(RMSE,Root Mean Square Error)时,可用很小的计算量大大提高测向精度,具有良好的工程应用价值。      

基于降维处理的MUSIC二维DOA估计算法

针对 MUSIC(多重信号分类法)估计方法实现二维 DOA(波达方向)估计的计算量大且遍历搜索耗时的问题,给出了一种基于降维处理的 MUSIC 算法。该算法无需进行二维谱峰值搜索。该算法利用二次优化方法将二维 DOA 估计分解为一维 DOA 估计,先通过一维 MUSIC 估计获得信号与 x 轴夹角,再利用最小二乘算法估计获得信号与 y 轴夹角。最后利用角度关系式得到信号的二维 DOA 估计值。该算法的复杂和搜索范围都大大降低,仿真表明,该算法具有较好的角度估计效果。     

基于MUSIC算法的相位干涉仪测向

提出了将相位干涉仪与MUSIC相结合的算法,先利用相位干涉仪算法对目标进行定位,然后在一定的范围内利用MUSIC算法进行谱峰搜索,这样既提高了L阵相位干涉仪的测角精度同时又缩小了搜索时间,有效地提高了MUSIC算法的效率。同时通过对L阵相位干涉仪、相关干涉仪、MUSIC谱估计和文中提出的联合算法的测向精度和MATLAB虚拟环境中的耗时仿真,结论表明此方法可以有效地解决精度高和实时性好这一矛盾。     

一种不需要特征值分解的MUSIC新算法

在阵列信号处理中,诸如MUSIC和ESPRIT等高分辨率空间谱估计算法都要对阵列输出数据的协方差矩阵逃行特征似分解,其计算量较大,不适合实时处理。因此,文中提出MCA（次分量分析）高效迭代算法,用来逼近噪声子空间,该算法无需进行特征值分解,计算过程相对简单,具有自组织特性,算法稳定收敛,适合于神经网络来实现,通过仿真实验证实了所提算法的优良性能及其可实施性。     

A systolic array architecture for the discrete sine transform

An efficient approach to design very large scale integration (VLSI) architectures and a scheme for the implementation of the discrete sine transform (DST), based on an appropriate decomposition method that uses circular correlations, is presented. The proposed design uses an efficient restructuring of the computation of the DST into two circular correlations, having similar structures and only one half of the length of the original transform; these can be concurrently computed and mapped onto the same systolic array. Significant improvement in the computational speed can be obtained at a reduced input-output (I/O) cost and low hardware complexity, retaining all the other benefits of the VLSI implementations of the discrete transforms, which use circular correlation or cyclic convolution structures. These features are demonstrated by comparing the proposed design with some of the previously reported schemes.     

基于L型阵列酉变换矩阵重构的二维DOA估计

二维空间信号波达方向（DOA）的估计是阵列信号处理的一个关键研究问题。经典的二维 MUSIC算法固然精度高,但此算法需要二维谱峰搜索,运算较为复杂。提出一种用于L型阵列的二维DOA估计算法,通过矩阵重构使得阵列输出矩阵变为中心对称矩阵,再利用酉变换矩阵将其由复值矩阵变为实值矩阵。该方法可以直接得到目标参数,不需要谱峰搜索,使得运算量大大降低。相比于 L 型阵列适用的增广矩阵束（MEMP）算法,该算法可以估计更多信源的DOA,并能获得较高的分辨率。计算机仿真结果表明,该算法具有较高的DOA估计精度。     

基于MUSIC算法的L型阵列MIMO雷达降维DOA估计

该文提出一种基于MUSIC算法的L型阵列多输入多输出雷达降维波达方向(DOA)估计算法。该算法首先针对L型阵列导向矢量的结构,构造出一个降维矩阵,将回波信号转换到低维空间。然后利用二次优化方法将2维DOA估计分解为两个1维DOA估计。最后利用MUSIC空间谱估计其中1维角度,并利用求得的角度回代谱函数,对另1维角度进行求根估计。该算法将2维空间谱搜索降为1维搜索,极大地降低了运算复杂度。理论分析和仿真结果验证了该算法的准确性和可行性。     

基于对数螺旋阵的波达方向估计

系统渡达方向(DOA)的估计能力受天线阵的几何结构影响很大.提出了一种新的二维天线阵列流形--对数螺旋阵,该阵列流形具有天线单元数少、天线孔径大、分辨力高的特点.通过粒子群优化方法(PSO)确定了无栅瓣的阵结构参数,计算了此阵的天线方向图并与矩形阵、圆环阵做了比较,利用MUSIC方法进行了二维空间谱估计,仿真结果表明:利用该对数螺旋多天线阵可以实现高分辨二维DOA估计.