A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation

TL;DR

This paper introduces a Differential Evolution algorithm with Neighborhood Mutation (DE-NM) that efficiently estimates multiple DOA peaks, reducing computational cost while maintaining high accuracy for real-time array signal processing.

Contribution

The paper proposes a novel DE-NM algorithm reformulating peak-finding as a multimodal optimization problem, enabling faster DOA estimation without dense grid sampling.

Findings

Achieves comparable accuracy to traditional grid search methods.

Significantly reduces computational time in simulations.

Suitable for real-time high-resolution DOA estimation.

Abstract

Two-dimensional (2D) Multiple Signal Classification algorithm is a powerful technique for high-resolution direction-of-arrival (DOA) estimation in array signal processing. However, the exhaustive search over the 2D an-gular domain leads to high computa-tional cost, limiting its applicability in real-time scenarios. In this work, we reformulate the peak-finding process as a multimodal optimization prob-lem, and propose a Differential Evolu-tion algorithm with Neighborhood Mutation (DE-NM) to efficiently lo-cate multiple spectral peaks without requiring dense grid sampling. Simu-lation results demonstrate that the proposed method achieves comparable estimation accuracy to the traditional grid search, while significantly reduc-ing computation time. This strategy presents a promising solution for real-time, high-resolution DOA estimation in practical applications. The imple-mentation code is available at https://github.com/zzb-nice/DOA_multimodel_optimize.