色噪声环境下MIMO雷达角度估计算法

针对色噪声环境下的MIMO雷达目标角度估计问题,提出一种基于四阶累积量切片的角度估计算法。算法利用MIMO雷达的接收数据计算出四阶累积量,构造出累积量切片矩阵,通过特征分解,结合ESPRIT算法实现了雷达目标的角度估计。同时进行了低复杂度改进,去掉了冗余信息,保留了MIMO雷达阵元扩展能力和目标分辨力,具有自动抑制加性高斯噪声和任意高斯色噪声的能力。最后计算机仿真结果证实了算法的有效性和可行性。     

冲击噪声背景下双基地MIMO 雷达目标角度估计方法

实际工作环境中雷达经常会受到琢稳态分布冲击噪声的影响,使得大量高斯噪声背景下基于二阶或更高阶累积量的角度估计算法性能急剧下降。文中提出了一种基于FLOM-ESPRIT算法的双基地MIMO雷达收发角度估计方法,算法通过利用匹配滤波后数据构造分数低阶协方差矩阵实现对冲击噪声的有效抑制,弥补了传统的二阶或四阶统计模型及相应的处理算法不适用于冲击噪声环境的缺陷,增强了信号子空间估计算法的稳健性,并且无需计算量繁杂的二维谱峰搜索。     

四阶累积量稀疏表示的DOA估计方法

针对现有稀疏重构DOA估计算法不能抑制噪声项以及在高斯色噪声背景下不再适用的问题,本文提出了基于四阶累积量稀疏重构的DOA估计方法。首先,利用接收数据的四阶累积量构建了稀疏表示模型,该模型抑制了噪声项;其次对四阶累计量矩阵进行奇异值分解,化简了稀疏表示模型,通过奇异值分解,不仅减小了数据规模,而且进一步抑制了噪声。对于稀疏表示模型的求解,先利用信号子空间与噪声子空间的正交特性选取权值矢量,然后利用加权l1范数法对模型求解实现DOA估计。理论分析和仿真实验表明本文算法在高斯白噪声和色噪声背景下均适用;能够处理非相干和相干信号,且在低信噪比条件下,对相干信号有更高的估计精度;较之同类的稀疏重构算法,本文算法具有较低的算法复杂度和更高的角度分辨力。      

基于MUSIC和ESPRIT的双基地MIMO雷达角度估计算法

该文基于2阶和4阶统计量,提出了空间高斯白噪声和高斯色噪声的背景下联合MUSIC和ESPRIT的双基地MIMO雷达角度估计算法。在接收端,通过单天线的MUSIC算法和双天线的ESPRIT算法分别估计目标的离开方向(Direction Of Departure, DOD)和波达方向(Direction Of Arrival, DOA),且DOD和DOA自动配对。该方法充分利用了MIMO雷达阵列孔径扩展的特征和ESPRIT的子空间旋转不变性,将2维参数估计问题转化为两个1维形式,降低了运算量和系统复杂度。计算机仿真验证了该方法的有效性。     

高斯色噪声背景下的单通道信源数目估计算法

目前信源数目估计算法大都是基于多通道接收模型且对高斯色噪声抑制能力较差,而实际应用中单通道接收模型及色噪声环境非常普遍,因此研究色噪声背景下的单通道信源数目估计算法意义重大。针对现有算法的缺陷提出了一种基于构建信号时间快拍和四阶累积量矩阵的单通道信源数目估计算法。首先通过构建信号时间快拍实现单通道接收信号的升维得到矢量化空间,然后以此组信号空间构造出四阶累积量矩阵,并从理论上验证了该四阶累积量矩阵能有效抑制高斯白噪声及高斯色噪声的影响,最后对该矩阵进行奇异值分解并通过信息论准则估计出信源个数。仿真实验和实际信号实验都表明本文算法能较好地解决单通道信源数目估计问题,且能有效抑制高斯色噪声。      

双基地MIMO雷达相干分布式目标快速角度估计算法

该文提出一种基于双基地MIMO雷达的相干分布式目标快速角度估计算法。建立了双基地MIMO雷达相干分布式目标信号模型;然后,基于该信号模型证明了相干分布式目标导向矢量具有Hadamard积旋转不变性;最后,利用该特性得到了对目标2维收发中心方位角的估计。分析表明:该算法无需搜索,参数配对简单,能有效降低算法的计算量;由于没有对相干分布式目标的角信号分布函数做固定的假设,所以该算法适用于具有不同角信号分布函数或角信号分布函数未知的情况,具有较强的稳健性。计算机仿真结果证明了该文算法的正确性和有效性。     

基于协方差匹配SLO算法的MIMO雷达DOA估计

利用加权平滑l0范数(Smoothed l0, SL0)算法估计MIMO雷达目标DOA时,需要把协方差矩阵进行矢量化来获得相应的稀疏重构模型,并利用信号和噪声子空间的正交性来构造加权向量。然而当存在相干信源时,MIMO雷达协方差矩阵的秩将退化,这会使得稀疏重构模型的误差较大以及无法正确区分信号和噪声子空间,导致加权SL0算法的DOA估计性能恶化。针对上述问题提出了一种基于协方差匹配SL0算法的MIMO雷达DOA估计方法。该方法利用协方差匹配准则重构出一个满秩的协方差矩阵,恢复MIMO雷达协方差矩阵的Toeplitz特性,并利用协方差逆矩阵的高阶幂来近似噪声子空间从而计算加权向量。仿真分析表明,该方法能够在无需预知信源数目的情况下有效地完成对相干信号的DOA估计。     

基于累积量的互耦误差下相干信号DOA估计

在高斯色噪声和阵列互耦误差背景下,针对相干信号源和非相干信号源并存的问题,提出了一种准确估计信号源到达角(DOA)的算法。首先,采用辅助阵元法将互耦误差从阵列流形中分离;然后结合空间平滑技术和四阶累积量构建平滑矩阵,实现对高斯噪声的抑制和对信号的解相干;最后使用ESPRIT算法获得信号源的来波方向。仿真结果表明,文中算法有效解决了阵列互耦和信源相干的影响,在高斯白噪声和高斯色噪声环境下均能精确地估计DOA。     

互耦条件下基于4阶累积量的双基地MIMO雷达角度估计

发射和接收阵列的互耦效应将使得双基地多输入多输出(MIMO)雷达的角度估计算法性能下降。针对阵列互耦效应和高斯色噪声并存情况,该文提出一种基于4阶累积量组合矩阵构造的收发角度估计方法。该方法首先根据收发互耦矩阵的Kronecker乘积特点,并结合互耦矩阵带状、对称的Toeplitz变换性质,充分利用所有的接收数据,构造出多组发射和接收4阶累积量矩阵,通过组合收发累积量矩阵进一步构造出4阶块累积量矩阵,并利用矩阵的奇异值分解,提取出发射和接收旋转不变因子。理论和仿真结果表明:在强互耦效应情况下,所提算法能够有效估计出高斯色噪声背景下目标的收发角度,并实现自动配对。在强互耦情况下,所提算法的估计性能优于其他算法。     

互耦条件下基于4阶累积量的双基地MIMO雷达角度估计

发射和接收阵列的互耦效应将使得双基地多输入多输出(MIMO)雷达的角度估计算法性能下降.针对阵列互耦效应和高斯色噪声并存情况,该文提出一种基于4阶累积量组合矩阵构造的收发角度估计方法.该方法首先根据收发互耦矩阵的Kronecker乘积特点,并结合互耦矩阵带状、对称的Toeplitz变换性质,充分利用所有的接收数据,构造出多组发射和接收4阶累积量矩阵,通过组合收发累积量矩阵进一步构造出4阶块累积量矩阵,并利用矩阵的奇异值分解,提取出发射和接收旋转不变因子.理论和仿真结果表明:在强互耦效应情况下,所提算法能够有效估计出高斯色噪声背景下目标的收发角度,并实现自动配对.在强互耦情况下,所提算法的估计性能优于其他算法.     

Cumulant-based approach to harmonic retrieval and related problems

A frequently encountered problem in signal processing is that of estimating the frequencies and amplitudes of harmonics observed in additive colored Gaussian noise. In practice, the observed signals are contaminated with spatially and temporally colored noise of unknown power spectral density. A cumulant-based approach to these problems is proposed. The cumulants of complex processes are defined, and it is shown that specific 1-D slices of the fourth-order cumulant of the noisy signal for the direction of arrival (DOA) and retrieval of harmonics in noise (RHN) problems are identical to the autocorrelation of a related noiseless signal. Hence correlation-based high-resolution methods may be used with fourth-order cumulants as well. The effectiveness of the proposed methods is demonstrated through standard simulation examples     

THE FOURTH-ORDER CUMULANTS BASED SPECTRALESTIMATION METHOD AND ITS APPLICATIONTO DIRECTION-FINDING

Traditional approaches of spatial spectral estimation are usually based on the second-order statistics. The higher-order cumulants and the poly-spectrum contain more information and are capable of reducing the Gaussian noise. In this paper, we present a new spectrum estimation method for direction-finding, the FOMUSIC algorithm, which is based on the eigen-structure analysis of the fourth-order cumulants. The derivation of the algorithm is given in detail and its performance is illustrated by both the computer simulations and the experiments of a direction-finding system. The obtained results demonstrate that the fourth-order cumulants based method outperforms the traditional methods, especially when the noise is an unknown colored one.     

基于四阶累积量对角切片的短波自适应通信信号检测

短波自适应通信信号的检测是现代电子战信号处理的一个重要研究课题,本文提出了基于四阶累积量对角切片的短波自适应通信信号检测方法.文章推导了有限长度信号四阶累积量对角切片的分段估计方法,分析了其渐进性能,分析表明该估计方法降低了估计方差;论文介绍了信号检测原理,借助于计算机仿真研究了高斯白噪声和色噪声及不同信噪比下不同检测方法的性能.仿真对比结果验证了该方法的有效性.     

房间环境下基于四阶累积量的语音时延估计

主要研究了利用四阶累积量在房间环境下的语音信号时延估计。根据模拟实验环境和实际实验环境下的仿真结果,分别比较了在高斯白噪声情况下与有色高斯噪声情况下基于二阶统计量的广义互相关法和基于四阶累积量的时延估计法性能,验证了四阶累积量法的有效性。仿真结果表明四阶累积量法具有很好的鲁棒性。     

Harmonic retrieval using higher order statistics: a deterministicformulation

Given a single record, the authors consider the problem of estimating the parameters of a harmonic signal buried in noise. The observed data are modeled as a sinusoidal signal plus additive Gaussian noise of unknown covariance. The authors define novel higher order statistics-referred to as “mixed” cumulants-that can be consistently estimated using a single record and are insensitive to colored Gaussian noise. Employing fourth-order mixed cumulants, they estimate the sinusoid parameters using a consistent, nonlinear matching approach. The algorithm requires an initial estimate that is obtained from a consistent, linear estimator. Finally, the authors examine the performance of the proposed method via simulations     

Applications of cumulants to array processing. II. Non-Gaussiannoise suppression

The main motivation of using higher order statistics in signal processing applications has been their insensitivity to additive colored Gaussian noise. The main objection to those methods is their possible vulnerability to non-Gaussian noise. The authors investigate the effects of non-Gaussian ambient noise on cumulant-based direction-finding systems using the interpretation for the information provided by cumulants for array processing applications described in Dogan and Mendek. they first demonstrate the suppression of uncorrelated non-Gaussian noise that has spatially varying statistics. Then, they indicate methods to suppress spatially colored non-Gaussian noise using cumulants and an additional sensor whose measurement noise component is independent of the noise components of the original array measurements. They also indicate the noise suppression properties of the virtual-ESPRIT algorithm proposed in Dogan and Mendel. In addition, they propose a method that combines second- and fourth-order statistics together in order to suppress spatially colored non-Gaussian noise. Finally, they also illustrate how to suppress spatially colored non-Gaussian noise when the additional sensor measurement is not available. Simulations are presented to verify the results     

一种新的近场源三维参数联合估计算法

本文研究近场源距离、频率和到达角(DOA)三维参数的联合估计问题，并提出一种计算有效的新算法。该算法利用特征值及相应的特征矢量估计信号参数，不需要谱峰搜索且各参数自动配对。此外，新算法使用四阶累积量，因此适用于任意的加性高斯噪声环境。计算机仿真证实了所提算法的有效性。     

Harmonic retrieval in colored non-Gaussian noise using cumulants

This paper addresses the harmonic retrieval problem in colored linear non-Gaussian noise of unknown covariance and unknown distribution. The assumptions made in the reported studies that the non-Gaussian noise is asymmetrically distributed and no quadratic phase coupling occurs are released. Using the elaborately defined fourth-order cumulants of the complex noisy observations, which are obtained by Hilbert transform, we can estimate either the correlation or the AR polynomial of the non-Gaussian noise via cumulant projections or ARMA modeling; then, the prewhitening or prefiltering techniques can be employed to retrieve harmonics, respectively. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms