Papers › A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation
A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation
Bo Zhou, Kaijie Xu, Yinghui Quan, Mengdao Xing
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.
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