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

被引：0
|
作者：
Liu Y. [1 ]
Sheng W. [1 ]
Hu B. [1 ]
Zhang L. [1 ]
机构：
[1] Air Defense Early Warning Equipment Department, Air Force Early Warning Academy, Wuhan
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2020年 / 41卷 / 03期
关键词：
Beam scheduling; Hybrid genetic particle swarm optimization; Long-term return rate; Markov decision process; Phased array radar; Unscented Kalman Filter (UKF); Waveform parameter optimization;
D O I：
10.7527/S1000-6893.2019.23519
中图分类号：
学科分类号：
摘要：
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%. © 2020, Press of Chinese Journal of Aeronautics. All right reserved.
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