Orthogonal Least Squares with Integrated Information Theoretic Criteria for Joint Number of Targets and DoA Estimation

TL;DR

This paper introduces three integrated methods combining information theoretic criteria with orthogonal least squares for joint target number and DoA estimation, improving accuracy and computational efficiency.

Contribution

It proposes three novel ITC-OLS algorithms for joint estimation, with a hybrid method showing superior performance over existing approaches.

Findings

The hybrid ITC-OLS algorithm outperforms other variants in simulations.

The proposed methods effectively balance likelihood and complexity in target estimation.

Numerical results demonstrate improved accuracy and efficiency.

Abstract

We address the joint estimation of the number of targets and their direction-of-arrivals (DoAs) using antenna arrays. Target-number estimation can be formulated as a model-order selection problem and solved with the information theoretic criteria (ITC). The ITC minimize an objective function that balances a likelihood term and a complexity penalty. However, direct application of the ITC requires maximum-likelihood DoA estimates for each candidate model order, which is computationally prohibitive because it entails a multidimensional search over all angle combinations. To reduce complexity, many radar processing exploit greedy methods such as orthogonal least squares (OLS). In this paper, we explore three distinct methods to integrate the ITC model-order selection into the OLS estimation procedure for joint target-number and DoA estimation. Specifically, we propose the disjoint rank-based, the joint selection-based, and the hybrid rank-and-selection-based ITC-OLS algorithms. Each algorithm is derived under both the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) frameworks. Numerical simulations show that the proposed hybrid ITC-OLS algorithm consistently outperforms both the other proposed variants and a baseline method from the literature.