ESPRIT方法在MIMO阵列中的应用研究

针对双基地雷达的探测需要,文章研究了一种基于旋转不变子空间(ESPRIT:Estimation of signal parameters via rotational invariance techniques)的信号发射角(DOD:Direction of Departure)和信号接收角(DOA:Direction of Arrival)估计方法。该方法使用多输入多输出(MIMO:Multiple Input Multiple Output)阵列,利用发射和接收阵列的不变特性,分别估计出感兴趣目标的DOD和DOA,同时进行目标的收发角度配对。文章将该方法应用到MIMO阵列中,并通过计算机仿真验证了其有效性。     

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。     

基于矢量传感器MIMO雷达的DOD DOA和极化联合估计算法

该文研究了一种基于多输入多输出(MIMO)电磁矢量传感器阵列雷达目标波离角(DOD),波达角(DOA)和极化联合估计问题。提出一种新型矢量阵MIMO雷达系统模型,发射阵列采用常规阵元,而接收阵列采用电磁矢量传感器。在此基础上,该文提出4维MUSIC, ESPRIT和迭代1维MUSIC 3种联合参数估计算法。其中迭代1维MUSIC算法首先利用矢量传感器的内在结构特点获得目标DOA预估计,随后采用MUSIC算法对DOD和DOA分别进行1维搜索获得目标角度的高精度估计,最后给出一种基于ESPRIT的目标极化估计算法。迭代1维MUSIC算法可用于不规则阵列,对接收阵列约束较少,无需2维搜索及多维搜索,还可以利用矢量阵特点扩展阵列孔径提高DOA估计精度。此外,论文还推导了DOD, DOA和极化联合估计的CRB。仿真实验表明,与前两种算法相比,迭代1维MUSIC算法具有与CRB更接近的估计精度。     

干涉式矢量传感器MIMO雷达的DOD/DOA和极化联合估计

该文结合干涉测量理论与简化矢量传感器多输入多输出(MIMO)雷达,提出一种干涉式矢量传感器MIMO雷达的发射方位角(DOD)、接收方位角(DOA)和极化联合估计方法。利用干涉发射阵列的长、短基线空间平移不变性采用多分辨ESPRIT算法获取DOD高精度估计值;同理,利用矢量接收阵的多分辨特性得到高精度DOA估计值;利用与阵列结构无关的极化域旋转不变性求取极化辅角和极化相位差。最后给出随机信号源模型下的闭式克拉美罗界推导。该干涉矢量MIMO阵列,可同时获取MIMO雷达的波形分集和矢量传感器的极化分集,且在不增加阵元数和硬件复杂度情况下大大扩展有效孔径,提高了角度估计精度。另外简化矢量传感器减少了传统矢量传感器的互耦效应更有利于工程实现。仿真结果证明了该文多参量估计算法的有效性。     

基于矢量传感器的扩展孔径双基地MIMO雷达多目标定位算法

该文研究基于电磁矢量传感器的扩展孔径多输入多输出(MIMO)雷达多目标定位算法。提出了一种新型MIMO雷达系统,发射阵列采用常规阵元,而接收阵列采用电磁矢量传感器,且传感器间距大于半波长。算法首先采用ESPRIT算法获得目标波达角(DOA)高精度模糊估计,随后利用矢量传感器的内在结构特点结合子空间旋转不变性获得目标DOA低精度无模糊估计进行解模糊,从而得到目标高精度DOA估计。最后利用已知DOA信息,采用1维MUSIC算法获得目标波离角(DOD)高精度估计。与已有算法相比,该算法大大提高了MIMO雷达的目标定位精度,且无需配对和2维搜索,具有较低的运算量。仿真结果证明了所提算法的有效性,其估计精度与CRB界接近。     

空间色噪声背景下双基地多输入多输出雷达低仰角估计方法

针对多径效应的影响,该文提出一种空间色噪声背景下双基地多输入多输出(MIMO)雷达低仰角估计方法。首先对双基地MIMO雷达中低仰角目标的多径环境进行建模,同时考虑发射和接收端的镜面反射信号,并用空间色噪声模拟漫反射。然后利用协方差矩阵求差方法消除未知色噪声的影响,在发射端和接收端进行空间平滑对多径信号解相干,即进行空间差分平滑处理。最后利用酉变换旋转不变技术(ESPRIT)算法估计目标的发射角(DOD)和接收角(DOA)。该文指出特殊情况下空间差分平滑协方差矩阵缺秩的问题,并提出一种修正的空间差分平滑方法。该算法对阵元数要求不高,适用于未知噪声背景及低信噪比环境,并且解决DOD与DOA联合估计的角度兼并问题。仿真实验表明了所提算法的有效性。     

空间色噪声环境下基于时空结构的双基地MIMO雷达角度和多普勒频率联合估计方法

该文针对空间色噪声环境提出一种基于时空结构的双基地MIMO雷达角度和多普勒频率联合估计方法,并推导了基于时空结构时角度和多普勒频率估计的克拉美-罗界(CRB)。该方法在时域噪声为高斯白噪声的假设下,首先将不同时刻匹配滤波器输出进行互相关以消除空间色噪声的影响,然后将相邻时刻匹配滤波器输出的时间相位差作为时间旋转因子,采用ESPRIT方法估计目标的DOD(Direction Of Departure), DOA(Direction Of Arrival)和多普勒频率。该方法能够克服空间色噪声的影响,所估计参数自动配对且无阵列孔径损失,并且适用于发射和接收阵列不满足平移不变结构的情况。计算机仿真验证了该文所提方法的有效性。     

多基线数据融合的双基地MIMO雷达角度估计

提出了一种双基地MIMO雷达角度估计方法。在发射端利用ESPRIT算法获取长短基线的旋转不变因子,通过联合对角化提高离开角度(DOD)的估计精度。根据已估计的DOD,采用迭代算法得到接收导向矢量。针对等距线阵和非等距线阵,分别提出了基于范德蒙矩阵特性和解模糊的多基线联合到达角度(DOA)估计方法,避免了两维搜索且角度自动配对。计算机仿真验证了该方法的有效性和优越性。     

互质MIMO雷达的非圆信号降维DOA估计方法北大核心CSCD

针对传统互质阵列波达方向估计方法存在的自由度低、阵列孔径小、相位模糊等问题,提出了一种基于互质MIMO雷达的非圆信号降维波达方向(Direction of Arrival, DOA)估计方法。该方法结合了互质阵列与MIMO雷达的优点,利用非圆信号特性对阵列进行扩展,重构接收信号矩阵,然后进行降维处理,并利用噪声特征值的幂级数对噪声子空间进行修正,进一步提高算法精度。最后推导了文中方法的无相位模糊问题。仿真实验表明,文中方法能够有效避免相位模糊,大大提高自由度并扩大阵列孔径,与传统MUSIC算法以及互质阵列MUSIC算法相比,在估计成功率、DOA估计精度等方面均具有更好的性能。     

波形相关矩阵未知情况下单基地MIMO雷达中一种改进MUSIC的DOA估计算法

该文研究了波形相关矩阵未知情况下多输入多输出(MIMO)雷达中的角度估计问题,提出了一种单基地MIMO雷达中改进多重信号分类(MUSIC)的到达角(DOA )估计算法。该算法可以在波形相关矩阵未知的情况下工作且性能优于传统的传播算子(PM)和借助旋转不变技术估计信号参数(ESPRIT)算法以及基于接收信号重构的MUSIC算法。该文算法可以扩展到任意阵列结构的MIMO雷达中进行角度估计。该文还给出了单基地MIMO雷达中DOA估计的克拉美罗界(CRB)。仿真结果验证了该算法的有效性。     

Novel angle estimation for bistatic MIMO radar using an improved MUSIC

In this article, we study the problem of angle estimation for bistatic multiple-input multiple-output (MIMO) radar and propose an improved multiple signal classification (MUSIC) algorithm for joint direction of departure (DOD) and direction of arrival (DOA) estimation. The proposed algorithm obtains initial estimations of angles obtained from the signal subspace and uses the local one-dimensional peak searches to achieve the joint estimations of DOD and DOA. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, and is almost the same as that of two-dimensional MUSIC. Furthermore, the proposed algorithm can be suitable for irregular array geometry, obtain automatically paired DOD and DOA estimations, and avoid two-dimensional peak searching. The simulation results verify the effectiveness and improvement of the algorithm.     

Joint parameter estimation of DOD/DOA/polarization for bistatic MIMO radar

In the paper,polarization-sensitive array is exploited at the receiver of multiple input multiple output (MIMO) radar system,a novel method is proposed for joint estimation of direction of departure (DOD),direction of arrival (DOA) and polarization parameters for bistatic MIMO radars. A signal model of polarimetric MIMO radar is developed,and the multi-parameter estimation algorithm for target localization is described by exploiting polarization array processing and the invariance property in both transmitter array and receiver array. By making use of polarization diversity techniques,the proposed method has advantages over traditional localization algorithms for bistatic MIMO radar. Simulations show that the performance of DOD and DOA estimation is greatly enhanced when different states of polarization of echoes is fully utilized. Especially,when two targets are closely spaced and cannot be well separated in spatial domain,the estimation resolution of traditional algorithms will be greatly degraded. While the proposed algorithm can work well and achieve high-resolution identification and accurate localization of multiple targets.     

DOD and DOA estimation using the spatial smoothing in MIMO radar with the EmV sensors

This paper deals with the directions of departure (DOD) and directions of arrival (DOA) estimation of coherent and noncoherent targets in bistatic MIMO radar with the electromagnetic vector (EmV) sensors. The high-resolution eigenspace-based methods such as, estimation of signal parameters via rotational invariance technique (ESPRIT), multiple signal classification, etc., fails to estimate DOD and DOA of fully or partially correlated targets. In order to employ these methods, a new pre-processing method is developed based on the spatial smoothing in MIMO radar with the EmV sensors. Then, the directions are estimated using the ESPRIT algorithm. Monte-Carlo simulations are performed to investigate the estimation-accuracy and resolution-capability of the proposed approach, and to compare with no pre-processing and the existing method. The simulation result shows that, the proposed methodology improves the performance significantly.     

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.     

Unfolded coprime bistatic MIMO radar for joint DOD and DOA estimation: from viewpoint on aperture augment for sum coarray

Generally, the coprime linear array (CLA) consisting of two interleaved uniform linear subarrays can enlarge the array aperture to attain the better angle estimation performance compared with the uniform linear array (ULA). In this paper, we ulteriorly study the virtual sum coarray of the unfolded coprime (UC) bistatic multiple-input multiple-output (MIMO) radar whose transmitter and receiver array are both unfolded CLA from the viewpoint on the geometry and array aperture. The UC MIMO radar can be exploited to obtain the better joint direction of departure (DOD) and direction of arrival (DOA) estimation performance due to the larger array aperture. Furthermore, we propose an all sum coarray multiple signals classification (ASCA-MUSIC) algorithm for the UC MIMO radar. ASCA-MUSIC can fully exploit all the degrees of freedom (DOFs) in the sum coarray and can obtain the better estimation performance. We also prove that ASCA-MUSIC can avoid the phase ambiguity problem due to the coprime property. In addition, we devise a reduced complexity scheme for ASCA-MUSIC to reduce the high computational complexity and utilize Cramer–Rao Bound (CRB) as a benchmark for the lower bound on the root-mean-square error (RMSE) of unbiased angle estimation. Finally, the numerical simulations verify the effectiveness and superiority of the UC MIMO radar, ASCA-MUSIC and the reduced complexity scheme.     

基于阵列流型盲辨识的MIMO雷达二维DOD和二维DOA联合估计

MIMO（Multiple Input Multiple Output）雷达基于分集增益理念,使其相对于相控阵雷达,在目标探测、参数测量、多目标分辨及干扰识别和抑制等方面具有明显优势.目标角度估计是雷达目标参数测量的核心内容,也是雷达对空域目标进行定位和跟踪的前提.本文基于双L型阵列,提出了一种高精度低复杂度的双基地MIMO雷达二维离开角和二维到达角联合估计的新算法.通过对匹配滤波后的阵列接收数据进行子空间分解,实现了阵列流形矩阵的盲辨识,进而获得目标二维到达角和二维离开角的闭式解.所提算法估得的收发四维角（二维离开角和二维到达角）能够自动配对,与2-D ESPRIT（Two Dimensional Estimating Signal Parameters via Rotational Invariance Techniques）算法相比,运算复杂度约是其三分之一,角估计性能相当.仿真实验证明了所提算法以较低的运算复杂度,实现了对目标收发四维角的高精度联合估计.     

波束空间的双基地MIMO雷达定位方法

提出了一种基于波束空间的双基地多输入多输出(MIMO)雷达目标定位方法——B-ESPRIT算法。该算法可重构受波束空间变换破坏的发射和接收阵列旋转不变特性,从而可利用ESPRIT方法来得到对目标收发方位角的估计值。由于降低了处理数据的维数,所提的B-ESPRIT算法相比常规的阵元空间ESPRIT(E-ESPRIT)算法具有更小的运算量。此外,还详细分析讨论了算法的波束增益与目标角度的关系,并通过计算机仿真证明了分析的正确性。