A New Computationally Efficient Approach for High-Resolution DOA Estimation of WideBand Signals Using Compressive Sensing

Abstract

Most modern wideband Direction of Arrival (DOA) estimation methods report reasonable resolution at the expenses of high computational complexity. In this paper, an enhanced approach for wideband DOA estimation with high resolution and low computational requirements is introduced. The suggested approach is based on combining the Incoherent Signal Subspace Method (ISSM) with Compressive Sensing (CS). The CS is employed using deterministic chaotic sensing matrices to decrease the dimension of the measurement vector, and hence reduce the software complexity. The efficiency of the introduced technique in enhancing the DOA estimation efficiency is studied for Uniform Linear Antenna Array (ULA). Several evaluation metrics, including the spatial spectrum, the consumed time, and the Root Mean Square Error (RMSE) between estimated and actual DOAs when varying the Signal to Noise Ratio (SNR) and number of elements, are investigated to assess the performance of the proposed approach. Results reveal that the proposed ISSM with CS succeeds not only to achieve high DOA resolution for separating very closely spaced sources, but also to significantly reduce the computational complexity while keeping nearly the same estimation resolution. This demonstrates the effectiveness of the proposed DOA estimation approach in wideband real-time wireless systems.