相控阵雷达长时跟踪波束调度与波形优化策略

doi:10.7527/S1000-6893.2019.23519

Abstract

Abstract: Aiming at the problem of multi-target tracking beam scheduling and waveform parameter optimization control of phased array radar, a strategy of tracking beam scheduling and waveform parameter optimization based on Markov Decision Process (MDP) is proposed. The Unscented Kalman Filter (UKF) algorithm is used to estimate the state of the target. Firstly, the sequence decision problem of this paper is modeled as a Markov decision process, and the cost-effectiveness ratio and the long-term return rate of the resource are defined. Then, the current actual tracking error is intigrated as the reward function of MDP, and the optimization model of joint scheduling is given. Finally, the long-term decision problem is transformed into a dynamic programming algorithm structure, and a parallel hybrid genetic particle swarm optimization algorithm is proposed to solve the optimal strategy at each decision time. The simulation result shows the advanced nature of the strategy and the superiority of the optimization algorithm. Compared with the traditional "short-term" strategy, the tracking accuracy can be improved by 11.17%.

Key words: phased array radar, beam scheduling, waveform parameter optimization, Markov decision process, Unscented Kalman Filter (UKF), long-term return rate, hybrid genetic particle swarm optimization

CLC Number:

V19
TN958

LIU Yiming, SHENG Wen, HU Bing, ZHANG Lei. Long time tracking beam scheduling and waveform optimization strategy for phased array radar[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(3): 323519-323519.

References 29

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Long time tracking beam scheduling and waveform optimization strategy for phased array radar

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