Hybrid integration method for highly maneuvering radar target detection based on a Markov motion model |
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Institution: | 1. National Key Laboratory of Science and Technology on ATR, National University of Defense Technology, Changsha 410073, China;2. School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China |
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Abstract: | To detect highly maneuvering radar targets in low signal-to-noise ratio conditions, a hybrid long-time integration method is proposed, which combines Radon-Fourier Transform (RFT), Dynamic Programming (DP), and Binary Integration (BI), named RFT-DP-BI. A Markov model with unified range-velocity quantification is formulated to describe the maneuvering target’s motion. Based on this model, long-time hybrid integration is performed. Firstly, the whole integration time is divided into multiple time segments and coherent integration is performed in each segment via RFT. Secondly, non-coherent integration is performed in all segments via DP. Thirdly, 2/4 binary integration is performed to further improve the detection performance. Finally, the detection results are exported together with target range and velocity trajectories. The proposed method can perform the long-time integration of highly maneuvering targets with arbitrary forms of motion. Additionally, it has a low computational cost that is linear to the integration time. Both simulated and real radar data demonstrate that it offers good detection and estimation performances. |
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Keywords: | Binary integration Dynamic programming Long-time integration Maneuvering targets Markov motion model Radar target detection Radon-Fourier transform |
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