Physics Informed Time–Frequency Dual Branch Target Detection Method for Early-Warning Radar.
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| Title: | Physics Informed Time–Frequency Dual Branch Target Detection Method for Early-Warning Radar. |
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| Authors: | Ni, Yao1,2 (AUTHOR), Ma, Shengbo2 (AUTHOR), Jing, Kai2,3 (AUTHOR), Wen, Biyang1 (AUTHOR) bywen@whu.edu.cn, Yang, Dongxiao2,3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 10, p1644. 25p. |
| Subjects: | Radar targets, Data fusion (Statistics), Surveillance radar, Radar signal processing, Time-frequency analysis |
| Abstract: | Highlights: What are the main findings? A time–frequency dual-branch feature fusion strategy based on the attention mechanism, integrating time-domain amplitude-phase features and frequency-domain physical constraint features, effectively improves the detection performance of weak radar targets. The phase difference feature in the time domain between targets and noise/interference exhibits stronger discriminability than the original phase feature, which facilitates weak target detection in the network. What are the implications of the main findings? The proposed method significantly enhances weak target detection performance for early-warning radars in complex environments, which is equivalent to extending the effective detection range. The dual-branch time–frequency feature fusion framework has broad engineering application potential and provides a valuable reference for further research in radar target detection. Early-Warning Radar (EWR) is an advanced detection system capable of monitoring aerial targets over long distances with high precision, providing critical information support for defense security. However, EWR faces challenges such as a limited number of pulses, low coherent integration gain, small target Radar Cross Section (RCS), and complex clutter and electromagnetic interference environments. Conventional Constant False Alarm Rate (CFAR) detection algorithms struggle to effectively detect weak targets while maintaining an acceptable false alarm rate. To address these issues, this paper introduces a deep learning approach. A high target-clutter/interference/noise discriminative feature spectrum is obtained through phase difference transformation, upon which a dual-branch collaborative architecture network is constructed. In this architecture, the main network focuses on extracting spatiotemporal amplitude–phase characteristics, while the auxiliary branch implicitly mines the target's physical boundary features from frequency-domain echoes. Through a self-attention mechanism, the features from both branches are semantically aligned and fused. This method significantly enhances the weak target detection capability of EWR under the constraint of a controlled false alarm rate. Test results show that under the false alarm rate ranging from 10 − 3 to 10 − 4 , the SNR gain of the proposed algorithm is about 2∼5 dB, which is equivalent to increasing the radar detection range by 10∼30%. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A time–frequency dual-branch feature fusion strategy based on the attention mechanism, integrating time-domain amplitude-phase features and frequency-domain physical constraint features, effectively improves the detection performance of weak radar targets. The phase difference feature in the time domain between targets and noise/interference exhibits stronger discriminability than the original phase feature, which facilitates weak target detection in the network. What are the implications of the main findings? The proposed method significantly enhances weak target detection performance for early-warning radars in complex environments, which is equivalent to extending the effective detection range. The dual-branch time–frequency feature fusion framework has broad engineering application potential and provides a valuable reference for further research in radar target detection. Early-Warning Radar (EWR) is an advanced detection system capable of monitoring aerial targets over long distances with high precision, providing critical information support for defense security. However, EWR faces challenges such as a limited number of pulses, low coherent integration gain, small target Radar Cross Section (RCS), and complex clutter and electromagnetic interference environments. Conventional Constant False Alarm Rate (CFAR) detection algorithms struggle to effectively detect weak targets while maintaining an acceptable false alarm rate. To address these issues, this paper introduces a deep learning approach. A high target-clutter/interference/noise discriminative feature spectrum is obtained through phase difference transformation, upon which a dual-branch collaborative architecture network is constructed. In this architecture, the main network focuses on extracting spatiotemporal amplitude–phase characteristics, while the auxiliary branch implicitly mines the target's physical boundary features from frequency-domain echoes. Through a self-attention mechanism, the features from both branches are semantically aligned and fused. This method significantly enhances the weak target detection capability of EWR under the constraint of a controlled false alarm rate. Test results show that under the false alarm rate ranging from 10 − 3 to 10 − 4 , the SNR gain of the proposed algorithm is about 2∼5 dB, which is equivalent to increasing the radar detection range by 10∼30%. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18101644 |
