Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals
Multipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the cla...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/2024/5026821 |
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| _version_ | 1832559526113443840 |
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| author | Kang Yan Weidong Jin Yingkun Huang Zhenhua Li Pucha Song Ligang Huang |
| author_facet | Kang Yan Weidong Jin Yingkun Huang Zhenhua Li Pucha Song Ligang Huang |
| author_sort | Kang Yan |
| collection | DOAJ |
| description | Multipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time–frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high-dimensional image space and low-dimensional feature space, a two-dimensional feature space representing normal samples is constructed. A one-class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach. |
| format | Article |
| id | doaj-art-ed077c9bb58d4393b2fd14d5bbc7ea10 |
| institution | Kabale University |
| issn | 1751-9683 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-ed077c9bb58d4393b2fd14d5bbc7ea102025-02-03T01:29:50ZengWileyIET Signal Processing1751-96832024-01-01202410.1049/2024/5026821Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar SignalsKang Yan0Weidong Jin1Yingkun Huang2Zhenhua Li3Pucha Song4Ligang Huang5School of Electrical EngineeringSchool of Electrical EngineeringNational Supercomputing Center in ShenzhenDepartment of Electronic EngineeringSchool of Electronic Information and Electrical EngineeringBeijing Institute of Remote Sensing EquipmentMultipath interference in radar signals caused by sea, ground, and other environments poses significant challenges to the target detection, tracking, and classification capabilities of radar systems. Existing methods for radar signal identification require labeled samples and focus mainly on the classification of normal signals. However, in practice, anomalous samples (multipath interference signals) may be scarce and highly imbalanced (i.e., mostly normal samples). To address this problem, we propose a deep anomaly detection with attention (DADA) for semisupervised detection of multipath radar signals. The method transforms radar signals into time–frequency images and is trained exclusively on normal samples. The autoencoder architecture is extended with a feature extractor network to capture latent sample features. CBAM attention is introduced to improve feature extraction. By learning the distribution of normal samples in high-dimensional image space and low-dimensional feature space, a two-dimensional feature space representing normal samples is constructed. A one-class SVM then learns the boundary of normal samples for anomaly detection. Extensive experiments on radar signal datasets validate the effectiveness of the proposed approach.http://dx.doi.org/10.1049/2024/5026821 |
| spellingShingle | Kang Yan Weidong Jin Yingkun Huang Zhenhua Li Pucha Song Ligang Huang Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals IET Signal Processing |
| title | Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals |
| title_full | Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals |
| title_fullStr | Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals |
| title_full_unstemmed | Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals |
| title_short | Deep Anomaly Detection with Attention (DADA): A Novel Approach for Identifying Multipath Interference in Radar Signals |
| title_sort | deep anomaly detection with attention dada a novel approach for identifying multipath interference in radar signals |
| url | http://dx.doi.org/10.1049/2024/5026821 |
| work_keys_str_mv | AT kangyan deepanomalydetectionwithattentiondadaanovelapproachforidentifyingmultipathinterferenceinradarsignals AT weidongjin deepanomalydetectionwithattentiondadaanovelapproachforidentifyingmultipathinterferenceinradarsignals AT yingkunhuang deepanomalydetectionwithattentiondadaanovelapproachforidentifyingmultipathinterferenceinradarsignals AT zhenhuali deepanomalydetectionwithattentiondadaanovelapproachforidentifyingmultipathinterferenceinradarsignals AT puchasong deepanomalydetectionwithattentiondadaanovelapproachforidentifyingmultipathinterferenceinradarsignals AT liganghuang deepanomalydetectionwithattentiondadaanovelapproachforidentifyingmultipathinterferenceinradarsignals |