Greedy Capon Beamformer

TL;DR

The paper introduces the greedy Capon beamformer (GCB), an efficient algorithm for direction finding of narrow-band sources that builds interference covariance matrices iteratively, outperforming existing methods in accuracy and speed.

Contribution

The paper presents a novel greedy algorithm for direction finding that iteratively constructs the interference-plus-noise covariance matrix using Capon's principle.

Findings

GCB performs favorably compared to state-of-the-art algorithms.

GCB provides fast and accurate DOA estimates.

Numerical examples demonstrate its effectiveness across various settings.

Abstract

We propose greedy Capon beamformer (GCB) for direction finding of narrow-band sources present in the array's viewing field. After defining the grid covering the location search space, the algorithm greedily builds the interference-plus-noise covariance matrix by identifying a high-power source on the grid using Capon's principle of maximizing the signal to interference plus noise ratio while enforcing unit gain towards the signal of interest. An estimate of the power of the detected source is derived by exploiting the unit power constraint, which subsequently allows to update the noise covariance matrix by simple rank-1 matrix addition composed of outerproduct of the selected steering matrix with itself scaled by the signal power estimate. Our numerical examples demonstrate effectiveness of the proposed GCB in direction finding where it performs favourably compared to the state-of-the-art algorithms under a broad variety of settings. Furthermore, GCB estimates of direction-of-arrivals (DOAs) are very fast to compute.