Joint DOA and Non-circular Phase Estimation of Non-circular Signals for Antenna Arrays: Block Sparse Bayesian Learning Method

TL;DR

This paper introduces a novel block sparse Bayesian learning algorithm for non-circular signals to improve DOA estimation accuracy, especially for unknown NC phases, using a permutation-based model and a fast recovery algorithm.

Contribution

It develops a new BSBL framework with a permutation strategy for non-circular signals, enhancing DOA estimation performance for arbitrary unknown phases.

Findings

Demonstrates superior recovery performance in simulations.

Effective for arbitrary unknown NC phases.

Enables rapid signal recovery with FMLM algorithm.

Abstract

This letter proposes a block sparse Bayesian learning (BSBL) algorithm of non-circular (NC) signals for direction-of-arrival (DOA) estimation, which is suitable for arbitrary unknown NC phases. The block sparse NC signal representation model is constructed through a permutation strategy, capturing the available intra-block structure information to enhance recovery performance. After that, we create the sparse probability model and derive the cost function under BSBL framework. Finally, the fast marginal likelihood maximum (FMLM) algorithm is introduced, enabling the rapid implementation of signal recovery by the addition and removal of basis functions. Simulation results demonstrate the effectiveness and the superior performance of our proposed method.