A Reversed and Shift Sparse Array Scheme based on the Difference and Sum Co-array

TL;DR

This paper introduces a reversed and shift sparse array scheme based on the difference and sum co-array that enhances source identification and increases degrees of freedom for DOA estimation by reducing overlap in co-arrays.

Contribution

It proposes a novel RAS scheme that reduces overlap in difference and sum co-arrays, improving DOA estimation capabilities for nested and co-prime arrays.

Findings

Achieves longer consecutive virtual arrays compared to existing DSCA-based arrays.

Demonstrates improved DOA estimation performance through simulations.

Provides a closed-form expression relating physical sensors to virtual array sensors.

Abstract

The reversed and shift (RAS) sparse array scheme, which is based on the difference and sum co-array (DSCA) and remarkably enhances the capability of identifying sources, is proposed. For the original nested array (NA) or co-prime array (CPA), there exists a large overlap between its difference co-array and sum co-array, which prevents it from obtaining high degrees of freedom (DOFs). Motivated by this fact, the RAS scheme is designed for reducing the overlap while increasing the DOFs for direction of arrival (DOA) estimation. The proposed scheme is effective for both NA and CPA. The closed-form expression for the relationship between the number of physical sensors and the number of consecutive DSCA sensors is derived. Compared with some representative DSCA based sparse arrays, the proposed one can achieve longer consecutive virtual array. Simulation experiments are carried out to exhibit the enhanced DOA estimation performance of RAS scheme.