Utilization of Noise-Only Samples in Array Processing With Prior Knowledge

TL;DR

This paper introduces an improved array processing estimator that effectively uses noise-only samples and prior knowledge to enhance signal and DOA estimation, especially with limited data and challenging noise conditions.

Contribution

It presents a novel estimator that incorporates prior DOA knowledge and noise-only samples, outperforming existing methods in small sample and adverse noise scenarios.

Findings

Enhanced estimation accuracy with fewer samples

Better performance under poor signal conditions

Outperforms state-of-the-art estimators

Abstract

For array processing, we consider the problem of estimating signals of interest, and their directions of arrival (DOA), in unknown colored noise fields. We develop an estimator that efficiently utilizes a set of noise-only samples and, further, can incorporate prior knowledge of the DOAs with varying degrees of certainty. The estimator is compared with state of the art estimators that utilize noise-only samples, and the Cram\'{e}r-Rao bound, exhibiting improved performance for smaller sample sets and in poor signal conditions.