Direction of Arrival Estimation in Low-Grazing Angle: A Partial Spatial-Differencing Approach
This paper addresses a partial spatial-differencing (PSD) approach for direction of arrival (DOA) estimation in a low-grazing angle (LGA) condition. By dividing the sample covariance matrix into several column sub-vectors, we first form the corresponding reconstructed subarray covariance matrices (RSCMs). We then calculate the spatial differencing matrix for the noise parts of RSCMs, while the non-noise parts are kept completely. That is, we build a PSD matrix. Compared with the existing spatial smoothing and full spatial-differencing methods, the PSD approach can use all the data information of the sample covariance matrix and also suppress the effect of additive white or colored noise more effectively. Simulation results show that our method provides a higher estimation accuracy and resolution than the state-of-the-art methods.
partial spatial differencing, direction of arrival estimation, reconstructed subarray covariance matrices, spatial smoothing.