针对最小方差无畸变响应空间谱估计(MVDR)方法稳定性问题，提出一种基于相参累积预处理的空间谱估计方法.该方法采用复解析小波变换将接收阵拾取数据转换为一定频带的复解析数据，并按空间平滑处理思想对复解析数据进行分组处理；充分利用传感器数据相位信息，对各组复解析数据进行相移补偿和累积处理，获得一组信噪比含有量较高的数据，并在时域采用多点累积处理方式对新数据构造协方差矩阵；依据协方差矩阵自身的正交特性实现空间谱估计.数值仿真和实测数据处理结果表明：相比MVDR方法和对角加载MVDR方法，该方法通过对接收阵拾取数据进行时域复解析变换和相参累积预处理，改变了构建协方差矩阵的数据来源，通过多个采样点累积实现满秩协方差矩阵的稳定获取.该方法依据空间方位与各传感器数据之间的相位差关系，通过两次指数函数等列式求和运算处理，有效提高了空间谱估计的稳定性.
For the stability problem of spatial spectrum estimation based on the minimum variance distortionless response (MVDR) method, a kind of spatial spectrum estimation method based on coherent cumulative preprocessing is proposed. First, complex analytic data with a certain frequency band is transformed by complex analytic wavelet transform from the collected data of receiving array, and the sub-group data is obtained according to the idea of spatial smoothing from the complex analytic data. Next, making full use of the phase information of sensor data, a group of data with high signal-to-noise ratio is obtained by accumulating the complex analytic data of each sub-group after time delay compensation. Then, the covariance matrix of the new data is constructed via multi-point cumulative processing in time domain. Finally, spatial spectrum estimation is realized according to the orthogonal property of the covariance matrix. The processing results of numerical simulation and measured data show that, compared with the MVDR method and the diagonal loading MVDR method, the data source of constructing the covariance matrix is changed in this method through time domain complex analytic transform and coherent accumulation pretreatment. The full rank covariance matrix is stably obtained by multiple sampling points accumulation in this method. According to the relation of spatial bearing and the phase difference of the sensor data, this method can effectively improve the stability of spatial spectrum estimation via double exponential function addition.
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