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| Citation: | ZHANG Liwen, PAN Jian, ZHANG Youcheng, et al. Capturing temporal-dependence in radar echo for spatial-temporal sparse target detection[J]. Journal of Radars, 2023, 12(2): 356–375. doi: 10.12000/JR22228
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Capturing Temporal-dependence in Radar Echo for Spatial-temporal Sparse Target Detection
doi: 10.12000/JR22228
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Abstract
Existing data-driven object detection methods use the Constant False Alarm Rate (CFAR) principle to achieve more robust detection performance using supervised learning. This study systematically proposes a data-driven target detection framework based on the measured echo data from the ground early warning radar for low-altitude slow dim target detection. This framework addresses two key problems in this field: (1) aiming at the problem that current data-driven object detection methods fail to make full use of feature representation learning to exert its advantages, a representation learning method of echo temporal dependency is proposed, and two implementations, including unsupervised- and supervised-learning are given; (2) Low-altitude slow dim targets show extreme sparsity in the radar detection range, such unevenness of target-clutter sample scale causes the trained model to seriously tilt to the clutter samples, resulting in the decision deviation. Therefore, we further propose incorporating the data balancing policy of abnormal detection into the framework. Finally, ablation experiments are performed on the measured X-band echo data for each component in the proposed framework. Experimental results completely validate the effectiveness of our echo temporal representation learning and balancing policy. Additionally, under real sequential validation, our proposed method achieves comprehensive detection performance that is superior to multiple CFAR methods. -
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References
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- Figure 1. Radar echo temporal relation learning-based spatial-temporal sparse target detection
- Figure 2. The sliding-window-based echo splitting
- Figure 3. The schematic diagram of EchoDarwin
- Figure 4. The diagram of EchoDarwin for unsupervised temporal feature learning of radar echo
- Figure 5. The illustration of supervised Seq2Seq-based radar echo temporal feature learning method
- Figure 6. The illustration of target-clutter sample scale balance
- Figure 7. The target sample scale visualization of target-clutter sample scale balancing
- Figure 8. The illustration of training process for echo temporal relation-based radar target detection framework
- Figure 9. The target sample scale visualization of target-clutter sample scale balancing
- Figure 10. Example of detection results comparison with CFARs under sequential validation of real-measured data (in one radar scanning cycle)