Prior-Guided Lightweight Dual-Task Network for Composite Active Jamming Recognition and Time-Frequency Parameter Estimation in Radar Remote Sensing.
Saved in:
| Title: | Prior-Guided Lightweight Dual-Task Network for Composite Active Jamming Recognition and Time-Frequency Parameter Estimation in Radar Remote Sensing. |
|---|---|
| Authors: | Qiu, Tianyu1 (AUTHOR), Zan, Yinkai2,3 (AUTHOR), Li, Xiaoxiong1,3 (AUTHOR), Zhang, Xuepan1 (AUTHOR) xpzhang7@163.com, Peng, Enchao1,2 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 17, p2946. 33p. |
| Subjects: | Remote sensing by radar, Time-frequency analysis, Electromagnetic interference detection |
| Abstract: | Highlights: What are the main findings? A prior-guided lightweight dual-task framework is proposed, which jointly uses STFT images, multi-domain features, and decision-tree coarse priors for structured cognition of composite active jamming. The framework simultaneously performs jamming family recognition, jamming class recognition, composite attribute prediction, and component-level time-frequency mask estimation for copy, convolution, and noise jamming components. What are the implications of the main findings? The proposed method extends radar jamming recognition from single-label classification to a structured task integrating class recognition, component localization, and parameter estimation, providing more direct support for subsequent anti-jamming decisions. The lightweight network and prior-information fusion improve the practicality of the model under limited-sample and resource-constrained conditions, facilitating deployment on real radar platforms. Active jamming in complex electromagnetic environments can severely degrade radar remote sensing imaging and target detection, especially when deceptive and suppressive jamming components coexist. Existing deep learning methods usually formulate jamming recognition as a closed set classification task, which provides limited information about component superposition, time-frequency localization, and physical jamming parameters. To address these limitations, this paper proposes a prior-guided lightweight dual-task network for structured composite active jamming cognition. The proposed framework extracts multi-domain handcrafted features and decision tree based coarse priors from the received signal, and fuses them with short-time Fourier transform (STFT) time-frequency images through a confidence-gated MobileViT_CA-based network. The recognition branch predicts the jamming family, fine-grained class, and composite attributes, while the segmentation branch estimates component masks for copy, convolution, and noise components. A FiLM-conditioned mask refinement module further improves mask continuity and boundary quality, enabling the extraction of physical parameters such as bandwidth, center frequency, coverage duration, delay, slice width, and repetition interval. Experiments on a 22-class active jamming dataset show that the proposed method, built on a 1.92 M-parameter MobileViT_CA backbone, achieves 93.78% overall classification accuracy, 96.49% jamming family accuracy, and 89.17% accuracy on the 12 composite classes. The FiLM-conditioned mask refinement module improves the test-set mIoU from 67.55% to 82.93%, and most representative physical parameters are estimated with relative errors below 15%. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? A prior-guided lightweight dual-task framework is proposed, which jointly uses STFT images, multi-domain features, and decision-tree coarse priors for structured cognition of composite active jamming. The framework simultaneously performs jamming family recognition, jamming class recognition, composite attribute prediction, and component-level time-frequency mask estimation for copy, convolution, and noise jamming components. What are the implications of the main findings? The proposed method extends radar jamming recognition from single-label classification to a structured task integrating class recognition, component localization, and parameter estimation, providing more direct support for subsequent anti-jamming decisions. The lightweight network and prior-information fusion improve the practicality of the model under limited-sample and resource-constrained conditions, facilitating deployment on real radar platforms. Active jamming in complex electromagnetic environments can severely degrade radar remote sensing imaging and target detection, especially when deceptive and suppressive jamming components coexist. Existing deep learning methods usually formulate jamming recognition as a closed set classification task, which provides limited information about component superposition, time-frequency localization, and physical jamming parameters. To address these limitations, this paper proposes a prior-guided lightweight dual-task network for structured composite active jamming cognition. The proposed framework extracts multi-domain handcrafted features and decision tree based coarse priors from the received signal, and fuses them with short-time Fourier transform (STFT) time-frequency images through a confidence-gated MobileViT_CA-based network. The recognition branch predicts the jamming family, fine-grained class, and composite attributes, while the segmentation branch estimates component masks for copy, convolution, and noise components. A FiLM-conditioned mask refinement module further improves mask continuity and boundary quality, enabling the extraction of physical parameters such as bandwidth, center frequency, coverage duration, delay, slice width, and repetition interval. Experiments on a 22-class active jamming dataset show that the proposed method, built on a 1.92 M-parameter MobileViT_CA backbone, achieves 93.78% overall classification accuracy, 96.49% jamming family accuracy, and 89.17% accuracy on the 12 composite classes. The FiLM-conditioned mask refinement module improves the test-set mIoU from 67.55% to 82.93%, and most representative physical parameters are estimated with relative errors below 15%. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18172946 |
