基于二阶预处理的共轭扩展MUSIC算法

基于子空间概念的经典MUSIC算法虽具有较优的性能,但可测向信号数小于阵元数,测向精度仍不能满足某些场合的要求。针对这一问题,提出了基于二阶预处理的共轭扩展MUSIC算法,利用阵列输出的延迟相关函数及其共轭形成伪阵列输出,从而得到伪协方差矩阵,对其进行奇异值分解,得到类似于MUSIC算法的简洁的空间谱表达式,其极大值点对应的角度就是波达方向。仿真实验表明,新算法可对多于阵元数的信号进行测向,其分辨力和测角精度均优于MUSIC和MUSIC-like算法。

Conjugate extended MUSIC algorithm based on second-order preprocessing

Although the MUSIC algorithm which is basesd on the concept of subspace has excellent performance, its resolvable number of sources is smaller than that of sensors and its performance for direction finding can not satisfy the requirement of some applications. To overcome the aforementioned questions, a new direction finding algorithm which is based on second-order preprocessing is proposed. The basic idea is using the cross-correlation and its conjugate form to derive the pseudo-data matrix from which the pseudo covariance matrix is constructed. Then the directions of arrival (DOAs) of the signals are estimated by MUSIC algorithm. Simulation results show that the proposed algorithm can handle more sources than the number of sensors and that the angular precision and resolution performance of the proposed algorithm is better than MUSIC and MUSIC-like algorithm.