Electronics Optics & Control, Volume. 26, Issue 7, 70(2019)
A Method for Slow Moving Target Detection with Airborne TS-MIMO Radar Based on Sparse Super-Resolution Spectrum Estimation
Compared with traditional airborne phased array radar, the spatial-domain degrees-of-freedom of the airborne Transmit Subaperturing-Multiple Input Multiple Output (TS-MIMO) radar are multiplied, and the training samples required for Space-Time Adaptive Processing (STAP) are also significantly increased, so the performance degrades sharply in the actual non-uniform clutter environment, and slow moving targets cannot be detected. Different from the traditional STAP method, this paper proposes a slow moving target detection method based on two-dimensional space-time sparse super-resolution spectrum estimation. The method uses the sparse Bayesian learning algorithm to directly measure the range cell data for space-time spectrum estimation. Then, the main clutter component in the space-time super-resolution spectrum is set to zero based on the radar prior parameters, and finally the slow target can be detected in the angle-Doppler domain based onconventional constant false-alarm processing. The proposed method does not require training samples, so it can significantly improve the slow moving target detection capability of the onboard TS-MIMO radar in practical applications. Simulation experiments verify the effectiveness of the proposed method.
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LUO Jing, DUAN Guangqing, QI Xiaoguang, YUAN Huadong, XU Hong. A Method for Slow Moving Target Detection with Airborne TS-MIMO Radar Based on Sparse Super-Resolution Spectrum Estimation[J]. Electronics Optics & Control, 2019, 26(7): 70
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Received: Apr. 18, 2018
Accepted: --
Published Online: Jan. 6, 2021
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