On the Performance of Subspace Methods with Array Model Errors and Spatial Smoothing

摘要

In this paper, the effect of spatial smoothing (Forward smoothing and Forward-Backward smoothing) on the performance of subspace methods in the presence of Array Model errors for Direction-Of-Arrival (DOA) estimation is studied. Theoretical expressions for the Mean Squared Error (MSE) in DOA are obtained, based on a common framework of analysis. Simulations are carried out to substantiate the theory developed. For the cases considered, smoothing improves the performance of ESPRIT and Minimum-Norm method while it is not so for MUSIC.