相干分布式目标的方位估计

目标源之间的强相干性，引起接收数据的协方差矩阵的秩亏损，从而可能导致目标漏报。为了解决相干源的方位估计问题以及提高算法在低信噪比下的估计性能，本文提出一种改进的Toeplitz化方位估计方法，即子空间重构法。其原理是根据处理点目标相干源时的方法，对采样矩阵进行Toeplitz化解相干处理，并在此基础上对接收数据的协方差矩阵特征分解后得到的子空间进行重构，从而有效解决相干分布源的方位估计问题。仿真结果表明，该方法能够在低信噪比下提高算法的估计性能，并能有效地对相干源进行估计。

Direction of Arrival Estimation for Coherent Distributed Target Sources

The strong coherence between target sources leads to rank loss of covariance matrix of receiving data may, and hence cause incomplete target report. In order to estimate the direction of arrival （DOA） of coherent target sources and increase the estimation performance of the algorithm under low signal to noise ratio （ SNR）, an improved DOA estimation method of Toeplitz, namely, sub-spatial rebuild method is presented. The sampling matrix is treated by Toeplitz based on the method of dealing with dot target coherent distributed source, then the sub-spatial obtained after decomposing the characteristic of covarianee matrix of receiving data is rebuilt, and the DOA estimation of coherent distributed source is dealt with effectively. Simulation results show that the method can improve the estimation performance of the algorithm under low SNR and estimate effectively coherent distributed source.