A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908468025229312 |
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| author | Zhou, Bo Xu, Kaijie Quan, Yinghui Xing, Mengdao |
| author_facet | Zhou, Bo Xu, Kaijie Quan, Yinghui Xing, Mengdao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06020 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation Zhou, Bo Xu, Kaijie Quan, Yinghui Xing, Mengdao Signal Processing Neural and Evolutionary Computing 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. |
| title | A Differential Evolution Algorithm with Neighbor-hood Mutation for DOA Estimation |
| topic | Signal Processing Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2507.06020 |