Multi‐subband fusion algorithm based on autoencoder
Abstract A novel algorithm for multi‐subband signal fusion achieves performance superior to traditional all‐pole model, matrix pencil algorithm and deep‐neural‐network (Deep neural network (DNN)). The method uses a deep‐learning autoencoder more fully described as a multi‐subband fusion autoencoder...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-12-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12153 |
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| _version_ | 1832559552501907456 |
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| author | Yilin Jiang Liting Zhang Li Li Jinxin Li Yaozu Yang |
| author_facet | Yilin Jiang Liting Zhang Li Li Jinxin Li Yaozu Yang |
| author_sort | Yilin Jiang |
| collection | DOAJ |
| description | Abstract A novel algorithm for multi‐subband signal fusion achieves performance superior to traditional all‐pole model, matrix pencil algorithm and deep‐neural‐network (Deep neural network (DNN)). The method uses a deep‐learning autoencoder more fully described as a multi‐subband fusion autoencoder (MSFAE). This autoencoder comprises two parts: a multi‐subband encoder and a full‐band decoder. Full‐band echo distance envelopes are used as training data for the full‐band autoencoder, to obtain the full‐band coding and the full‐band decoder. Then, the multi‐subband echo distance envelopes are used as training data, and the full‐band coding is used as labels, to train the multi‐subband encoder. Finally, the multi‐subband encoder and the full‐band decoder are combined to obtain the MSFAE. The multi‐subband distance envelopes are input to the MSFAE to obtain the full‐band distance envelopes, improving the radar distance resolution and obtaining high‐resolution range profiles. In contrast with the traditional all‐pole model and matrix pencil algorithm, the authors’ MSFAE directly processes the information in the frequency domain, avoiding the error of pole estimation in the echo domain. In contrast with DNN, the authors’ MSFAE needs only multi‐subband distance envelopes as input, avoiding noise subband redundancy. The experimental results show that the fusion accuracy of MSFAE is higher than the traditional all‐pole model, matrix pencil algorithm and DNN. The MSFAE has superior performance using the fusion method, even at low signal‐to‐noise ratio. |
| format | Article |
| id | doaj-art-4db528a159b6483da48a28bb9d74349c |
| institution | Kabale University |
| issn | 1751-9675 1751-9683 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-4db528a159b6483da48a28bb9d74349c2025-02-03T01:29:44ZengWileyIET Signal Processing1751-96751751-96832022-12-011691071108410.1049/sil2.12153Multi‐subband fusion algorithm based on autoencoderYilin Jiang0Liting Zhang1Li Li2Jinxin Li3Yaozu Yang4College of Information and Communication Engineering Harbin Engineering University Harbin ChinaCollege of Information and Communication Engineering Harbin Engineering University Harbin ChinaDepartment of Information System Dalian Naval Academy Dalian ChinaCollege of Information and Communication Engineering Harbin Engineering University Harbin ChinaCollege of Information and Communication Engineering Harbin Engineering University Harbin ChinaAbstract A novel algorithm for multi‐subband signal fusion achieves performance superior to traditional all‐pole model, matrix pencil algorithm and deep‐neural‐network (Deep neural network (DNN)). The method uses a deep‐learning autoencoder more fully described as a multi‐subband fusion autoencoder (MSFAE). This autoencoder comprises two parts: a multi‐subband encoder and a full‐band decoder. Full‐band echo distance envelopes are used as training data for the full‐band autoencoder, to obtain the full‐band coding and the full‐band decoder. Then, the multi‐subband echo distance envelopes are used as training data, and the full‐band coding is used as labels, to train the multi‐subband encoder. Finally, the multi‐subband encoder and the full‐band decoder are combined to obtain the MSFAE. The multi‐subband distance envelopes are input to the MSFAE to obtain the full‐band distance envelopes, improving the radar distance resolution and obtaining high‐resolution range profiles. In contrast with the traditional all‐pole model and matrix pencil algorithm, the authors’ MSFAE directly processes the information in the frequency domain, avoiding the error of pole estimation in the echo domain. In contrast with DNN, the authors’ MSFAE needs only multi‐subband distance envelopes as input, avoiding noise subband redundancy. The experimental results show that the fusion accuracy of MSFAE is higher than the traditional all‐pole model, matrix pencil algorithm and DNN. The MSFAE has superior performance using the fusion method, even at low signal‐to‐noise ratio.https://doi.org/10.1049/sil2.12153autoencoderdeep learningHRRPneural networksubband fusion |
| spellingShingle | Yilin Jiang Liting Zhang Li Li Jinxin Li Yaozu Yang Multi‐subband fusion algorithm based on autoencoder IET Signal Processing autoencoder deep learning HRRP neural network subband fusion |
| title | Multi‐subband fusion algorithm based on autoencoder |
| title_full | Multi‐subband fusion algorithm based on autoencoder |
| title_fullStr | Multi‐subband fusion algorithm based on autoencoder |
| title_full_unstemmed | Multi‐subband fusion algorithm based on autoencoder |
| title_short | Multi‐subband fusion algorithm based on autoencoder |
| title_sort | multi subband fusion algorithm based on autoencoder |
| topic | autoencoder deep learning HRRP neural network subband fusion |
| url | https://doi.org/10.1049/sil2.12153 |
| work_keys_str_mv | AT yilinjiang multisubbandfusionalgorithmbasedonautoencoder AT litingzhang multisubbandfusionalgorithmbasedonautoencoder AT lili multisubbandfusionalgorithmbasedonautoencoder AT jinxinli multisubbandfusionalgorithmbasedonautoencoder AT yaozuyang multisubbandfusionalgorithmbasedonautoencoder |