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| Citation: | ZHU Hangui, FENG Weike, FENG Cunqian, et al. Deep unfolding based space-time adaptive processing method for airborne radar[J]. Journal of Radars, 2022, 11(4): 676–691. doi: 10.12000/JR22051
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Deep Unfolding Based Space-Time Adaptive Processing Method for Airborne Radar
doi: 10.12000/JR22051
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Abstract
The Sparse Recovery Space-Time Adaptive Processing (SR-STAP) method can use a small number of training range cells to effectively suppress the clutter of airborne radar. The SR-STAP approach may successfully eliminate airborne radar clutter using a limited number of training range cells. However, present SR-STAP approaches are all model-driven, limiting their practical applicability due to parameter adjustment difficulties and high computational cost. To address these problems, this study, for the first time, introduces the Deep Unfolding/Unrolling (DU) method to airborne radar clutter reduction and target recognition by merging the model-driven SR method and the data-driven deep learning method. Firstly, a combined estimation model for clutter space-time spectrum and Array Error (AE) parameters is established and solved using the Alternating Direction Method of Multipliers (ADMM) algorithm. Secondly, the ADMM algorithm is unfolded to a deep neural network, named AE-ADMM-Net, to optimize all iteration parameters using a complete training dataset. Finally, the training range cell data is processed by the trained AE-ADMM-Net, jointly estimating the clutter space-time spectrum and the radar AE parameters efficiently and accurately. Simulation results show that the proposed DU-STAP method can achieve higher clutter suppression performance with lower computational cost compared to typical SR-STAP methods. -
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References
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- Figure 1. Geometry model of airborne radar
- Figure 2. The data flow graph of ADMM algorithm
- Figure 3. The network structure of AE-ADMM-Net
- Figure 4. Four sub-layers of AE-ADMM-Net
- Figure 5. Clutter space-time spectra and array error parameters estimated via ADMM algorithm with fixed parameters (a1—a4: Clutter space-time spectra estimation results in different array error parameters, b1—b4: Amplitude error estimation results in different array error parameters, c1—c4: Phase error estimation results in different array error parameters)
- Figure 6. Convergence performance of AE-ADMM-Net and its comparison with ADMM algorithm
- Figure 7. Clutter space-time spectra estimated via different algorithms under different conditions (a1—a4: estimation results of ADMM algorithm with 25 iterations in different array error parameters, b1—b4: estimation results of ADMM algorithm with 45 iterations in different array error parameters, c1—c4: estimation results of FOCUSS algorithm with 200 iterations in different array error parameters, d1—d4: estimation results of SBL algorithm with 400 iterations in different array error parameters, e1—e4: estimation results of AE-ADMM-Net with 25 layers in different array error parameters, f1—f4: estimation results of AE-ADMM-Net with 45 layers in different array error parameters)
- Figure 8. Array error parameters estimated by AE-ADMM-Net under different conditions (a1—a4: Amplitude error estimation results in different array error parameters, b1—b4: Phase error estimation results in different array error parameters)
- Figure 9. SCNR loss curves corresponding to different methods under different conditions (a1—a4: SCNR loss curves results in different array error parameters, b1—b4: SCNR loss curves results with enlarged scale in different array error parameters)
- Figure 10. Running time of different algorithms under different conditions
- Figure 11. Processing results of Mountain Top actual measured data