A Novel Sparse recovery based DOA estimation algorithm by relaxing the RIP constraint

TL;DR

This paper introduces a modified OMP algorithm for DOA estimation that relaxes the RIP constraint, effectively handling coherent sources and improving accuracy with only one snapshot.

Contribution

A novel sparse recovery algorithm that relaxes RIP constraints in OMP for better DOA estimation of coherent and uncorrelated sources using a single snapshot.

Findings

Outperforms standard OMP in coherent source scenarios

Reduces sparsity requirement for accurate source recovery

Provides a method to estimate source distance in array processing

Abstract

Direction of Arrival (DOA) estimation of mixed uncorrelated and coherent sources is a long existing challenge in array signal processing. Application of compressive sensing to array signal processing has opened up an exciting class of algorithms. The authors investigated the application of orthogonal matching pursuit (OMP) for direction of Arrival (DOA) estimation for different scenarios, especially to tackle the case of coherent sources and observed inconsistencies in the results. In this paper, a modified OMP algorithm is proposed to overcome these deficiencies by exploiting maximum variance based criterion using only one snapshot. This criterion relaxes the imposed restricted isometry property (RIP) on the measurement matrix to obtain the sources and hence, reduces the sparsity of the input vector to the local OMP algorithm. Moreover, it also tackles sources irrespective of their coherency. The condition for the weak-1 RIP on decreased sparsity is derived and it is shown that how the algorithm gives better result than the OMP algorithm. With an addition to this, a simple method is also presented to calculate source distance from the reference point in a uniform linear sensor array. Numerical analysis demonstrates the effectiveness of the proposed algorithm.