利用时空平均法改善DOA估计性能

在信号子空间类DOA估计算法中 ,自相关矩阵估计的优劣决定了DOA估计的性能 ,但是常规自相关矩阵估计方法是对阵列输出信号做时间平均。然而对于均匀线列阵这一特殊阵型 ,其阵列输出自相关矩阵的具有To eplitz结构。根据均匀线列阵信号的时空平稳特性 ,本文提出一种时空平均的方法来改善自相关矩阵的估计质量 ,即对阵列的自相关矩阵做时间和空间两次平均 ,从而提高了DOA估计的性参 ;仿真结果表明 :在低信噪比或低快拍数的条件下能够明显提高DOA估计性能

Improving performance of DOA estimation by using temporal-spatial averaging

In subspace DOA estimation method,the performance of DOA estimation is determined by quality of correlation matrix.The conventional method of correlation matrix estimation is to average the output of sensor array.For a uniform linear array.the structure of correlation matrix of array output is Hermitian and Toeplitz.Based on this property of correlation matrix,a new method is proposed to improve the quality of correlation matrix by using temporal spatial averaging.So the performance of DOA estimation is improved.The simulation results show that the performance of DOA estimation can be improved greatly,especially on the condition that signal to noise ratio is low and the number of snapshot is not enough.