A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar
To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/829 |
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| author | Bo Wang Tao Lai Qingsong Wang Haifeng Huang |
| author_facet | Bo Wang Tao Lai Qingsong Wang Haifeng Huang |
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| description | To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system. |
| format | Article |
| id | doaj-art-ab0eb09ca19f4f9a9e572646cba0476c |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-ab0eb09ca19f4f9a9e572646cba0476c2025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-02-0117582910.3390/rs17050829A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW RadarBo Wang0Tao Lai1Qingsong Wang2Haifeng Huang3School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, ChinaSchool of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, No. 66, Gongchang Road, Guangming District, Shenzhen 518107, ChinaTo achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system.https://www.mdpi.com/2072-4292/17/5/829periodic errorsneural networksAdaptive Moment Estimation (ADAM)Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS)matched Fourier transform |
| spellingShingle | Bo Wang Tao Lai Qingsong Wang Haifeng Huang A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar Remote Sensing periodic errors neural networks Adaptive Moment Estimation (ADAM) Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) matched Fourier transform |
| title | A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar |
| title_full | A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar |
| title_fullStr | A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar |
| title_full_unstemmed | A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar |
| title_short | A Nonlinear Compensation Method for Enhancing the Detection Accuracy of Weak Targets in FMCW Radar |
| title_sort | nonlinear compensation method for enhancing the detection accuracy of weak targets in fmcw radar |
| topic | periodic errors neural networks Adaptive Moment Estimation (ADAM) Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) matched Fourier transform |
| url | https://www.mdpi.com/2072-4292/17/5/829 |
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