基于四线性分解的双基地MIMO雷达的角度和多普勒频率联合估计

摘要

研究双基地多输入多输出(Multiple-input Multiple-output,MIMO)雷达中的角度和多普勒频率联合估计问题,提出了一种基于四线性分解(Quadrilinear Decomposition)的离开角(Direction of Departure,DOD)、波达角(Direction of Arrival,DOA)和多普勒频率的联合估计算法.通过对接收端匹配滤波器的输出进行延迟操作,得到符合四线性模型的数据,根据四线性交替最小二乘(Quadrilinear Alternating Least Squares,QALS)进行迭代,得到方向矩阵和多普勒频率矩阵的估计,进而得到角度和频率的估计.该算法无需谱峰搜索,无需知道反射系数,可实现角度和频率的自动配对,且能用于非均匀阵,该算法的角度估计性能优于多维ESPRIT方法和三线性交替最小二乘(Trilinear Alternating Least Squares,TALS)方法.论文分析了所提算法复杂度,并推导了克拉美-罗界(Cramer-Rao bound,CRB).仿真结果验证了该算法的有效性.%This paper studies the joint estimation of angle and Doppler frequency in a bistatic multiple-input multiple-output (MIMO) radar, and proposes a joint angle and Doppler frequency estimation algorithm using quadrilinear decomposition It reconstructed the delayed received-data to develop a quadrilinear data model. Using quadrilinear alternating least squares (QALS), it obtained an estimation of the direction matrix and the Doppler frequency matrix, and further the estimated angles and frequency via the least squares. The proposed algorithm requires no peek searching, does not need a priori knowledge of the reflection coefficients, and it can realize automatically paired angles and Doppler frequency. Furthermore, it is suitable for non-uniform arrays, and the angle estimation performance is better than that of multi-dimensional ESPRIT and triline-ar alternating least squares (TALS). The paper analyzes the complexity of the algorithm, and derives the Cramer-Rao bound (CRB). Simulation compares the proposed algorithm with other algorithms, and verifies its validity.