FDA-MIMO雷达实值降维求根MUSIC参数估计算法

Real-value Reducing-dimension Root-MUSIC Method in Monostatic FDA-MIMO Radar

提出了一种基于实值降维求根多重信号分类(MUSIC)的单基地频率分集阵列-多输入多输出(FDA-MIMO)雷达目标角度和距离估计方法。首先,利用前后向平均和酉变换技术,将复值目标数据模型转化为实值模型,然后利用特征值分解得到实值噪声子空间;利用克罗内克积的特性,将目标角度和距离联合估计问题分解为目标角度、距离分别求解的过程,实现二维MUSIC方法的降维;最后利用多项式求根求解出目标角度与距离参数。数值仿真结果验证了该算法的有效性,结果表明,本文算法目标估计精度与2D-MUSIC算法相当,但本文算法显著降低了计算复杂度,且比基于旋转不变技术的信号参数估计(ESPRIT)算法和传播算子(PM)算法具有显著提升的目标估计精度。

A real-value reducing-dimension root mutiple signal classification(MUSIC)algorithm is proposed for target angle and distance estimation in monostatic frequency diverse array multiple input multiple output(FDA-MIMO)radar.Firstly,the complex target echo data model is transformed into a real value model by using forward backward averaging and unitary transformation techniques.Then the real valued noise subspace is obtained by eigenvalue decomposition(EVD).According to the characteristics of Kronecker product,the joint estimation of target angle and distance is decomposed into the process of solving target angle and distance respectively,so as to reduce the dimension of 2 D-MUSIC algorithm.Finally,the target angle and distance parameters are solved by the polynomial rooting.Numerical simulation results verify the effectiveness of the algorithm.The results show that the proposed algorithm has the same estimation performance as 2 D-MUSIC algorithm,but the proposed algorithm significantly reduces the computational complexity and has better performance than estimating signal parameter via rotational invariance techniques(ESPRIT)algorithm and propagator method(PM)algorithm.