Sparse Sensing with Semi-Coprime Arrays

TL;DR

The paper introduces semi-coprime arrays (SCA), a highly sparse and efficient sensor array design that reduces sensor count and improves beampattern quality for direction of arrival estimation.

Contribution

The paper proposes semi-coprime arrays (SCA), a novel sparse array configuration that outperforms existing arrays in sensor savings and side lobe suppression.

Findings

SCA reduces the number of sensors compared to other sparse arrays.

SCA achieves beampatterns free of grating lobes.

SCA improves side lobe patterns in DOA estimation.

Abstract

A semi-coprime array (SCA) interleaves two undersampled uniform linear arrays (ULAs) and a $Q$ element standard ULA. The undersampling factors of the first two arrays are $QM$ and $QN$ respectively where $M$ and $N$ are coprime. The resulting non-uniform linear array is highly sparse. Taking the minimum of the absolute values of the conventional beampatterns of the three arrays results in a beampattern free of grating lobes. The SCA offers more savings in the number of sensors than other popular sparse arrays like coprime arrays, nested arrays, and minimum redundant arrays. Also, the SCA exhibits better side lobe patterns than other sparse arrays. An example of direction of arrival estimation with the SCA illustrates SCA's promising potential in reducing number of sensors, decreasing system cost and complexity in various signal sensing and processing applications.