Airborne Target Recognition of Narrowband Radar Short Time Observation Echoes Based on Feature Fusion

GUO Zekun; LIU Zheng; XIE Rong; RAN Lei; XU Hanzheng

doi:10.11999/JEIT231232

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GUO Zekun, LIU Zheng, XIE Rong, RAN Lei, XU Hanzheng. Airborne Target Recognition of Narrowband Radar Short Time Observation Echoes Based on Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231232

Citation:

GUO Zekun, LIU Zheng, XIE Rong, RAN Lei, XU Hanzheng. Airborne Target Recognition of Narrowband Radar Short Time Observation Echoes Based on Feature Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT231232

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Airborne Target Recognition of Narrowband Radar Short Time Observation Echoes Based on Feature Fusion

doi: 10.11999/JEIT231232

National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (62001346), The Stabilization Support of National Key Laboratory of Radar Signal Processing (KGJ202205)

Received Date: 2023-11-07
Rev Recd Date: 2024-03-13

Available Online: 2024-03-25

Abstract

Abstract

Narrowband radar is widely used in the field of air defense guidance due to its advantages of low cost and long operating range. With the development of high-speed mobile platforms, traditional target recognition methods based on feature modeling of long-term observation echo sequences are no longer applicable. In response to the problem of poor feature recognition ability of narrowband radar for Observe Echoes for a Short period of Time (OEST) sequences and susceptibility to bait target interference, resulting in low reliability of recognition results, a narrowband radar OEST sequence air target recognition method using multi feature adaptive fusion is proposed in this paper. Firstly, the encoder and classification layers are constructed with channel-spatial attention modules and trained to adaptively enhance features with high separability. Then, the maximum edge orthogonal loss function is proposed to increase the feature spacing between different classes, reduce the feature spacing between the same classes, and make the feature vectors orthogonal between different classes; Finally, the parameters of the encoder layer and classification layer are fixed, and the decoder layer is trained using reconstruction loss value to ensure that the model has accurate identification ability for decoy targets. Under the condition of an observation sequence length of 100, the classification accuracy and discrimination rate of the experimental part reached 94.37% and 96.78%, respectively. It can be concluded that the proposed method can effectively improve the classification performance of narrowband radar and the discrimination ability against bait targets, thereby improving the reliability of recognition results.
- Narrowband radar,
- High-speed mobile platform,
- Aerial target recognition,
- Observe Echoes for a Short period of Time (OEST),
- Feature fusion

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References(24)

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