MIMO雷达GMTI性能及DOA估计的克拉美·罗界分析

研究了MIMO雷达GMTI 性能及其DOA估计的克拉美·罗界(CRB)。首先建立机载平台下MIMO雷达信号模型,通过对系统参数设置,可使MIMO雷达工作在3种不同模式中。接着分析了MIMO雷达GMTI性能,并推导出了在MIMO雷达空时信号模型下,利用空时联合域信息时DOA估计的CRB。仿真结果表明,无论是GMTI性能还是DOA估计性能,MIMO雷达均优于相控阵雷达,尤其是发射稀疏阵MIMO雷达性能最佳。同时,利用空时联合域信息能有效改善MIMO雷达DOA估计的CRB,在目标多普勒频率相差较大时效果更加明显。     

基于最小冗余线阵的共轭循环ESPRIT算法

本文提出了一种基于最小冗余线阵的共轭循环ESPRIT算法。理论分析和计算机仿真实验均表明:该算法具有良好的DOA估计性能,增大了阵列孔径,抗噪能力强,分辨率高,可以用较少的阵元估计更多的信号源方向。计算机仿真实验验证了算法的有效性,并比较了该算法与共轭循环ESPRIT算法的DOA估计性能。     

相干激光探测中微动参数估计的克拉美-罗界

为选择最佳参数估计方法估计目标微多普勒特征,需要研究参数估计的克拉美-罗界,来评价各估计方法的性能。以相干激光探测为背景,考虑噪声方差未知的影响,严格推导了高斯白噪声环境下微动目标回波信号各参数估计的克拉美-罗界的闭合表达式,仿真分析了目标相对于雷达的位置信息、数据处理长度以及回波信噪比与参数估计方差下界的关系。结果表明,克拉美-罗界与噪声方差无关,目标相对于雷达的方位角、俯仰角越小,数据长度和信噪比越大,参数估计的方差下界越小。对目前常用的两种微动参数估计方法方差进行了计算,并与推导克拉美-罗界进行了对比。最后,与通过近似处理方法得到的克拉美-罗界进行了对比,指出了精确推导方差下界的意义。     

多子阵互耦条件下的一维波达方向估计及互耦白校正

该文研究多子阵（multiple subarrays）阵元互耦条件下的波达方向（DOA）估计,假设阵列由多个位置已知的均匀线阵（ULA）组成,但线阵阵元间存在互耦效应。利用均匀线阵互耦矩阵的带状、对称Toeplitz性及多子阵互耦矩阵的块状对角特性,提出了一种解耦合波达方向估计及互耦自校正算法。该算法在未知阵元互耦参数的情况下,可准确估计出信源的波达方向。另外,算法在精确估计波达方向的同时,还可准确估计出阵元问的互耦系数,实现多子阵的互耦自校正。算法的波达方向估计只需一维谱峰搜索,避免了通常多参数联合估计的多维非线性搜索及迭代运算,可明显减小算法运算量。文中讨论了算法参数可辨识性的必要条件,并分析计算了多参数联合估计的克拉美-罗界（CRB）。理论分析及蒙特卡罗仿真结果表明,该算法具有计算量小、DOA估计分辨力高、互耦校正效果好等优点。     

基于MUSIC及其改进算法的空间信号到达方向估计方法研究

本文首先介绍了多重信号分类(MUSIC)算法的原理和步骤,给出了不同快拍数和不同信噪比条件下基于MUSIC算法的空间信号到达方向(DOA)估计的仿真和分析,提出了DOA估计系统在工程设计中需要注意的问题和解决办法。随后,针对实际工程中复杂电磁环境下的信号源相干问题,研究了一种基于空间平滑的改进MUSIC算法的原理和步骤,并对传统MUSIC算法与改进MUSIC算法对相干信源的DOA估计性能进行了仿真对比和分析。仿真结果表明,与传统MUSIC算法相比,基于空间平滑改进的MUSIC算法能有效改善对相干信号源的测量性能,得到正确的DOA估计结果。     

一种基于CDMA系统的DOA估计算法

提出一种应用在码分多址(CDMA)智能天线系统中移动用户信号到达方向(DOA)的估计算法,该算法利用用户扩频码和基带信号的恒模特征,使得同时估计出的DOA个数不受天线阵元数的限制,运算量也很小。理论分析和计算机仿真都证明该算法是可行的,适合在信噪比较低的实时场合中使用。     

一种空间非均匀噪声环境下DOA估计的改进方法

文中提出一种空间非均匀噪声环境下的信号源DOA估计的改进方法.该算法首先对阵列协方差矩阵进行平滑处理,在此基础上估计出空间非均匀噪声协方差矩阵,通过已估计的噪声协方差矩阵对平滑处理后的阵列数据协方差矩阵进行白化处理,克服了空间非均匀噪声对空间谱估计的影响,实现信号源DOA估计.相对于原方法,文中所提方法改善了DOA估计的性能,计算量小,并且能够估计强相关或相干信号源DOA.理论分析和仿真结果均表明所提方法的有效性.     

基于解相干的改进MUSIC算法DOA估计

MUSIC算法是一种子空间分解算法。在非相干的情况下,经典MUSIC算法能准确进行波达方向（DOA）估计,但当信号源在相干的情况下时算法失效。在对经典MUSIC算法进行理论分析研究的基础上,对其阵元接收数据阵做相应变换,得到共轭重构后的协方差矩阵,通过特征值分解再进行DOA估计。就经典的MUSIC和改进算法的DOA估计性能进行了仿真分析。结果表明,改进后的算法在信号源相干的情况下也能精确地估计信号的波达方向。     

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。     

基于虚拟阵列变换的相干源测向方法

针对传统空间谱测向方法对相干信号源测向失效的实际问题,提出了一种基于任意形状平面阵列的测向方法。该方法在建立了相干信号源数学模型的基础上,对虚拟阵列变换思想进行了分析,给出了采用虚拟阵列变换原理和MUSIC算法实现了对相干信号源DOA的估计,提高了测向性能。经仿真试验和理论分析,验证了算法的可行性,并分析了该算法的适用局限性。     

Bounds on bearing and symbol estimation with side information

We develop Cramer-Rao bounds (CRBs) for bearing, symbol, and channel estimation of communications signals in flat-fading channels. We do this using the constrained CRB formulation of German and Hero (1990), and Stoica and Ng (see IEEE Signal Processing Lett., vol.5, p.177-79, 1998), with the unknown parameters treated as deterministic constants. The equality constraints may be combined arbitrarily, e.g., we may develop CRBs for bearing estimation of constant modulus (CM) signals where a subset of the symbols are known (semi-blind, CM case). The results establish the value of side information in a large variety of communications scenarios. We focus on the CM and semi-blind properties and develop closed-form CRBs for these cases. Examples are presented indicating the relative value of the training and CIM property. These show the significant amount of signal processing information provided under these two conditions. In addition, we consider the performance of the maximum-likelihood beamformer for the semi-blind case, assuming the bearings are known. This semi-blind beamformer achieves the appropriate (constrained) CRB with finite data at finite SNR. Analysis also reveals that in a semi-blind scenario with two closely spaced sources, ten or more training symbols are sufficient to achieve the asymptotic training regime. Together with previous results on angle estimation for known sources, these results indicate that relatively few training samples enable both angle estimation and closely spaced co-channel source separation that approaches the CRB with finite data and finite SNR     

Performance Bounds for Multisource Parameter Estimation Using a Multiarray Network

Networked sensors are increasingly being used to perform tasks such as detection, source localization, and tracking. It is intuitive to expect a performance increase by completing these tasks with networked arrays. Quantifying the increase in performance requires a generalized analysis, which is typically done using the Cramer-Rao bounds (CRB). Previously, the CRB for multisource-single-array and multiarray-single-source models were derived, but not the general multiarray-multisource case. In this paper, the general case is modeled and is shown to reduce to previously published cases. The model and CRB derived in this paper serve as a benchmark to which current and future research in multiarray, multisource parameter estimation algorithms can be compared. It is proven that the CRB of a scalar parameter in a model containing K + 1 sources is always higher than the CRB of K sources. In addition, using numerical analysis, it is shown that adding sources to a multiarray model has unique effects that cannot be predicted from less general models. The number of sources and sensors, the geometry of the model (i.e., source and sensor locations), and the source and noise power levels all affect the CRB. The effect of changing model parameters is shown for several multiarray-multisource examples when the estimated parameter is source location.     

校正源方位存在偏差时的幅相误差顽健校正算法

针对校正源方位偏差会影响幅相误差校正精度这一问题,在假设校正源方位偏差的概率分布已知的条件下,依据子空间拟合准则和Bayesian估计理论框架,给出了一种抑制校正源方位偏差的幅相误差顽健校正算法。该算法可在无需估计校正源方位的情况下,通过计算某实对称矩阵最小特征值对应的特征向量获得幅相误差的顽健估计。推导了参数估计的CRB(Cramer-Rao bound),分析了顽健算法的渐近性能以及Cheng方法在校正源方位有偏差时的渐近性能。理论分析和仿真实验均表明:在一定条件下,所提出的算法的渐近性能可达到CRB,并且优于Cheng方法的渐近性能(当校正源方位有偏差时)。     

Direction-of-arrival estimation for constant modulus signals

In many cases where direction finding is of interest, the signals impinging on an antenna array are known to be phase modulated and, hence, to have a constant modulus (CM). This is a strong property; by itself, it is already sufficient for source separation and can be used to construct improved direction finding algorithms. We first derive the relevant Cramer-Rao bounds (CRBs) for arbitrary array configurations and specialize to uniform linear arrays. We then propose a simple suboptimal direction estimation algorithm in which the signals are separated using the CM property followed by direction finding on the decoupled signals. Compared with the ESPRIT algorithm and the CRB for arbitrary signals, the algorithm shows good results     

Directions of arrival estimation with planar antenna arrays in the presence of mutual coupling

Directions of arrival (DoAs) estimation of multiple sources using an antenna array is a challenging topic in wireless communication. The DoAs estimation accuracy depends not only on the selected technique and algorithm, but also on the geometrical configuration of the antenna array used during the estimation. In this article the robustness of common planar antenna arrays against unaccounted mutual coupling is examined and their DoAs estimation capabilities are compared and analysed through computer simulations using the well-known MUltiple SIgnal Classification (MUSIC) algorithm. Our analysis is based on an electromagnetic concept to calculate an approximation of the impedance matrices that define the mutual coupling matrix (MCM). Furthermore, a CRB analysis is presented and used as an asymptotic performance benchmark of the studied antenna arrays. The impact of the studied antenna arrays geometry on the MCM structure is also investigated. Simulation results show that the UCCA has more robustness against unaccounted mutual coupling and performs better results than both UCA and URA geometries. The performed simulations confirm also that, although the UCCA achieves better performance under complicated scenarios, the URA shows better asymptotic (CRB) behaviour which promises more accuracy on DoAs estimation.     

抑制校正源方位估计偏差的阵元位置误差鲁棒估计算法

针对校正源方位估计偏差会影响阵元位置误差校正精度这一问题,该文在假设校正源方位估计偏差的概率分布先验已知的条件下,根据信号子空间拟合理论和贝叶斯估计理论框架,提出一种能够抑制校正源方位估计偏差的阵元位置误差鲁棒估计算法.该算法以Newton迭代的形式给出,并不需要对校正源方位进行数值优化.理论分析和仿真实验均表明:在一定条件下,文中的新算法对校正源方位估计偏差具有较好的鲁棒性,并且其渐近性能可逼近相应的克拉美罗界.     

Reactance-domain ESPRIT algorithm for a hexagonally shaped seven-element ESPAR antenna

A direction-of-arrival (DoA) method that combines the reactance-domain (RD) technique and the ESPRIT algorithm is proposed for use with the 7-element electronically steerable parasitic array radiator (ESPAR) for the estimation of noncoherent sources. Simulations show that the method could resolve up to three incoming signals with an estimation performance that depends on the signal's angle of arrival. Moreover, the method is compared with the Cramer-Rao lower bound (CRB) and the MUSIC asymptotic error variance, both modified for the RD technique. Numerical comparison between this lower bound and the MUSIC algorithm confirmed that the proposed method can achieve the CRB and provide high-precision DoA estimation with a level of performance that is sufficient for many DoA finding applications. The proposed method could be demonstrated by means of experiments on DOA estimation conducted in an anechoic chamber.     

基于稀疏协方差矩阵迭代的单快拍气流速度估计算法

该文研究基于声传感器阵列的单快拍气流速度估计问题。首先,根据声波在亚音速和超音速气流中的传播特性,针对特定的测量装置,建立了声传感器线性阵列的输出模型。在此基础上,提出一种稀疏协方差矩阵迭代的单快拍(Sparse Covariance Matrix Iteration with a Single Snapshot, SCMISS)气流速度估计算法,与其他稀疏估计方法相比,该文提出的SCMISS算法无需正则化参数选择,计算量更低,具有更强的实时性,且只需单快拍采样数据就可对亚音速和超音速气流速度进行统一估计。最后,为了评价所提算法的估计性能,推导了气流速度估计的克拉美-罗界(Cramr-Rao Bound, CRB)表达式。仿真实验验证了该算法的有效性。     

Joint Angle and Array Gain-Phase Errors Estimation Using PM-like Algorithm for Bistatic MIMO Radar

This paper discusses the problem of the direction of departure (DOD) and the direction of arrival (DOA) estimation for multi-input multi-output (MIMO) radar with array gain-phase errors. In this paper, we propose a propagator method (PM)-like algorithm for joint angle and array gain-phase errors estimation in MIMO radar. The proposed method not only yields automatically paired estimates of the angles and gain-phase errors but also has much better gain-phase errors estimation performance than the estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm; this has higher computational cost than the proposed algorithm. Furthermore, the proposed algorithm has angle estimation performance very close to ESPRIT-like algorithm. We also derive the Cramér–Rao bound (CRB) for MIMO radar with array gain-phase errors. Simulation results present the usefulness of our approach.     

基于双向传播算子的互质面阵二维波达方向估计

针对谱峰搜索的二维波达方向估计中现有算法复杂度高,精度受搜索间隔影响较大的问题,给出了一种双向传播算子的互质面阵二维波达方向估计算法,实现了俯仰角和方位角的低复杂、高精度、无模糊联合估计.该方法首先将互质阵列引入到二维波达方向估计中,构造互质平面阵模型,然后采用两次旋转不变传播算子方法计算出不同阵列流型方向上的旋转因子矩阵,根据旋转因子矩阵解算出目标信号的俯仰角和方位角,同时利用互质理论消除了稀疏阵列角度估计的不确定性,证明了互质阵列模型下采用双向传播算子方法进行俯仰角和方位角估计的无模糊性.对算法的复杂度进行理论分析,并给出了平面阵列角度估计的克拉美罗界推导.理论分析与仿真结果表明,算法不需要进行角度匹配和谱峰搜索,在相同条件下的均方根误差性能优于均匀平面阵的多重信号分类算法,并且以较低的复杂度无模糊的达到了高维网格搜索的精度.