低复杂度的MIMO声呐协方差矩阵重构方法*

多输入多输出声纳在对目标进行测向时会产生复杂的运算量,从而降低算法的测向效率。针对这一问题,提出了一种基于降维变换方法的低复杂度协方差矩阵重构方法。该方法能够抑制噪声,提高目标侧向性能。首先利用降维变换方法对接收信号进行波束形成,获得低维度的协方差矩阵,再对矩阵进行Toeplitz处理,抑制矩阵的相干性。所得到的新的协方差矩阵,通过特征分解获得噪声子空间和信号子空间,利用MUSIC方法进行测向。为了进一步降低运算复杂度,利用阵型所满足的旋转不变性,可以采用ESPRIT算法对目标进行波达方向估计。理论分析和实验结果表明,该方法有效降低了运算复杂度,提高了算法的测向性能。在有限快拍数的情况下,与传统测向方法相比,具有运算速度快,目标分辨力强的特点。

A low complexity covariance matrix reconstruction method of MIMO sonar

Multiple-input multiple-output (MIMO) will generate complex operation when it is used to do direction finding on targets, which will lead to the reduction of algorithm efficiency. To solve this problem, a low complexity covariance matrix reconstruction method based on dimension reduction transformation is proposed. This method can help to suppress noise and improve direction finding performance. Firstly, utilize the dimension reduction transformation method to beam form the echo signals, we can obtain a low dimensional covariance matrix. Then, do Toeplitz processing on the matrix to suppress coherence. We can achieve the noise subspace and signal subspace by characterizing the new covariance matrix we obtain. MUSIC algorithm can be used to do direction finding. For further reduce the computational complexity, we can adopt the ESPRIT algorithm to do direction of arrival (DOA) estimation, by utilizing the rotation invariance satisfied by the MIMO array. Both theoretical analysis results and numerical simulation results verify that this algorithm effectively reduces the computational complexity, improve the direction finding performance. In the condition of limited snapshots, compared with the traditional direction finding methods, it has the characteristics of fast calculation speed and strong target resolution.