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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.01398 |
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| _version_ | 1866909884013871104 |
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| author | Hao, Wang Zhang, Kuang Chengyu, Hou Chenxing, Tan Weiming, Cui Weifeng, Fu Xinran, Yao |
| author_facet | Hao, Wang Zhang, Kuang Chengyu, Hou Chenxing, Tan Weiming, Cui Weifeng, Fu Xinran, Yao |
| contents | Compared to real-valued signals, complex-valued signals provide a unique and intuitive representation of the phase of real physical systems and processes, which holds fundamental significance and is widely applied across many fields of science and engineering. In this paper, we propose a robust modal decomposition (RMD) in the complex domain as a natural and general extension of the original real-valued RMD. We revisit and derive the mathematical principles of RMD in the complex domain, and develop an algorithmic version tailored for this domain. Extensive experiments are conducted on synthetic simulation datasets and real-world datasets from diverse fields, including a millimeter-wave radar physiological signal detection dataset, a faulty bearing dataset, a radio-frequency unmanned aerial vehicle identification dataset, and a WiFi CSI-based respiration detection dataset. The results demonstrate that the proposed complex-domain robust modal decomposition significantly improves performance across these various applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_01398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CRMD: Complex Robust Modal Decomposition Hao, Wang Zhang, Kuang Chengyu, Hou Chenxing, Tan Weiming, Cui Weifeng, Fu Xinran, Yao Signal Processing Compared to real-valued signals, complex-valued signals provide a unique and intuitive representation of the phase of real physical systems and processes, which holds fundamental significance and is widely applied across many fields of science and engineering. In this paper, we propose a robust modal decomposition (RMD) in the complex domain as a natural and general extension of the original real-valued RMD. We revisit and derive the mathematical principles of RMD in the complex domain, and develop an algorithmic version tailored for this domain. Extensive experiments are conducted on synthetic simulation datasets and real-world datasets from diverse fields, including a millimeter-wave radar physiological signal detection dataset, a faulty bearing dataset, a radio-frequency unmanned aerial vehicle identification dataset, and a WiFi CSI-based respiration detection dataset. The results demonstrate that the proposed complex-domain robust modal decomposition significantly improves performance across these various applications. |
| title | CRMD: Complex Robust Modal Decomposition |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2511.01398 |