空间非平稳噪声下的宽带DOA估计算法

投影子空间正交性测试(TOPS)法是利用子空间的正交性实现宽带信号DOA估计,而在空间非平稳噪声环境下子空间的正交性条件不再满足,尤其是在低信噪比或低快拍条件下子空间估计将出现较大误差,TOPS算法性能将急剧下降。针对该问题,提出了一种空间非平稳噪声下宽带DOA估计算法。该算法首先通过构造特殊对角矩阵将噪声从数据协方差矩阵中剔除,从而克服非平稳噪声对DOA估计的影响;然后利用平方TOPS法实现宽带信号DOA估计,消除了传统TOPS算法中的伪峰。该算法适用于空间非平稳噪声背景及低信噪比环境,提高了对角度相近目标的分辨性能;仿真实验表明了该算法的有效性。

Direction of Arrival Estimation of Wideband Signals with Sensor Arrays in Spatial Non-stationary Noise

The test of orthogonality of projected subspaces (TOPS) estimates directions of arrival of wideband sources by measuring the orthogonal relation between the signal and the noise subspaces of multiple frequency components of the sources. It is known that classical subspace-based directions-of-arrival ( DOA) estimation algorithms are not straightforwardly applicable to scenarios with unknown spatially non-stationary noise. For a small number of samples or low signal-to-noise ratio (SNR), such methods are exposed to performance breakdown, as the sample covariance matrix can largely deviate from the true covariance matrix. A new method for estimating DOA of multiple spatial wideband signals in the presence of spatially non-uniform is presented. The modified covariance matrix is obtained by introducing a diagonal matrix composed of the main diagonal elements of the sample data covariance matrix. The method removes the entries involving noise variances to obtain a noise-free covariance matrix for DOA estimation. Squared-TOPS use the transformed signal subspaces once again and noise subspaces twice and performs the squared test of orthogonality of signal subspaces and noise subspaces. The advantage of the proposed method is that the resolution and RMSE has been improved from that of TOPS in low SNR. The simulation results demonstrate the effectiveness of the proposed method.