On-grid adaptive compressive sensing framework for underdetermined DOA estimation by employing singular value decomposition

Abstract

In the field of Array Signal Processing, the problem of Direction of Arrival (DOA) estimation has attracted colossal attention of researchers in the past few years. The problem refers to estimating the angle of arrival of the incoming signals at the receiver end, from the knowledge of the received signal itself. Generally, an array of antenna/sensors is employed at the receiver for this purpose. In over-determined DOA estimation, the number of signal sources, whose direction needs to be estimated are usually lesser than half the number of antenna array elements, whereas the challenge is to estimate the DOAs in under-determined case, where the signal source number is quiet larger than the number of antenna array elements. This paper tackles such a problem by the application of multiple level nested array. Instead of subspace-based techniques for the estimation, sparse signal representation for Compressive Sensing (CS) framework is used, which eliminates the requirement of prior information about the source number and also the tedious task of computing the inverse of the covariance matrices. In this paper, we propose an adaptive approach for Least Absolute Shrinkage and Selection Operator (LASSO) with reduced number of computations by singular value decomposing of the received signal vector. The outcomes of this paper showcase that the presented algorithm achieves high degree of freedom (DOF), good resolution, minimum root mean square error and less computational complexity with increased speed of estimation.