A Novel Direction Finding Method Based on Compressed Least-Squared Regression

The conventional direction finding method is to digitize the output of each sensor at the Nyquist sampling rate for the system bandwidth and use digital signal processing algorithms such as multiple signals classification (MUSIC) or maximum likelihood estimation (MLE) etc. In this paper, we exploit recent developments in compressed sensing (CS) to efficiently perform the direction finding. If the targets sparsely populated, direction of arrival can be obtained from a much smaller number of measurements. And this paper presents a novel algorithm based on compressed least-squares regression, it consider a compressed feature space instead of a compressed data space, we provide performance comparison for the existing methods and the proposed method, Computer simulation results show the effectiveness of the proposed method.