四阶累积量阵列扩展的传播算子测向方法

Propagator method for direction of arrival estimation based on an array extension of the fourth-order cumulant

针对MUSIC-LIKE方法中累积量矩阵存在大量冗余数据的问题,提出了一种四阶累积量与最小冗余线阵相结合的传播算子测向方法.分析了四阶累积量进行阵列扩展的原理,通过将四阶累积量应用于最小冗余线阵,摒弃了原MU-SIC-LIKE方法应用于均匀线阵中的大量冗余数据,并且获得了比MUSIC-LIKE更大的阵列扩展能力,同时结合传播算子方法,用线性运算代替特征分解求得噪声子空间,减小了计算量,缩短运算时间.论证了四阶累积量适用于最小冗余线阵及传播算子方法的原理,仿真实验验证了其在阵列扩展、测向性能及计算量的优越性.

An unacceptably large quantity of redundant data enters the cumulant matrix when estimating direction of arrival （DOA） of an impulse with multiple signal classification （MUSIC）-like methods. To solve this problem, a new propagator method for DOA estimation was proposed that combined a fourth order cumulant and a minimum re- dundancy linear array. Analysis was done on the principle of increasing the virtual array elements based on the fourth order cumulant. The massively redundant data of the fourth-order cumulant matrix in uniform linear arrays can be eliminated by the application of a minimum redundancy linear array. This results in an improved capacity for array expansion. The propagation method, which used a linear operator in place of eigen-decomposition, was used to reduce computational complexity and computing time. Improvements in its ability to extend arrays, in DOA esti- mation performance and computational speed were illuminated by computer simulations. Keywords： DOA estimation ; array extension ; propagator method ; fourth-order cumulant