Task scheduling algorithm for phased array radar based on shifting impact rate

被引：1
|
作者：
Duan Y. [1 ,2 ]
Tan X. [1 ]
Qu Z. [1 ]
Wang H. [1 ]
Wang P. [3 ]
机构：
[1] Air Force Early Warning Academy, Wuhan
[2] Unit 95174 of the PLA, Wuhan
[3] Unit 94201 of the PLA, Jinan
来源：
| 1600年 / Chinese Institute of Electronics卷 / 39期
关键词：
Gentic algorithm (GA); Kalman filter; Phased array radar; Shifting impact rate (SIR); Task scheduling; Time shifting;
D O I：
10.3969/j.issn.1001-506X.2017.11.12
中图分类号：
学科分类号：
摘要：
In phased array radar task scheduling, the object function is often based on the time shifting rate (TSR). However, the TSR cannot achieve the optimal scheduling performance in nonlinear filter systems.For this question, this paper, based on the analysis of the influence of time shifting, proposes the shifting improve rate, calculate the formula of shifting impact rate (SIR) in Kalman filter (KF) and extend Kalman filter (EKF), and design the objective function of phased array radar task scheduling based on SIR index.Then the improved genetic algorithm is used to solve this problem in order to speed up the convergence rate.Simulation scenario is used to test the scheduling method.The experimental results show that in the linear filtering system, the SIR is similar to the TSR; but in the nonlinear filtering system, the scheduling performance and tracking accuracy of SIR is better than TSR. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2470 / 2476
页数：6
相关论文
共 24 条
  • [11] Challa S., Morelande M.R., Musicki D., Fundamentals of Object Tracking, pp. 19-23, (2015)
  • [12] Severson T.A., Distributed optimization of resource allocation for search and track assignment with multifunction radars, (2013)
  • [13] Hao L., Yang X., Hu S., Task scheduling of improved time shifting based on genetic algorithm for phased array radar, Proc. of the IEEE, International Conference on Signal Processing, pp. 1655-1660, (2017)
  • [14] Zheng Y.J., Tian K.S., Xing X.N., Optimal scheduling for phased array radar based on nivhe genetic algorithm, Modern Defence Technology, 44, 1, pp. 168-173, (2016)
  • [15] Wang S., He J., Wang B., Et al., Research on adaptive scheduling algorithm based on improved genetic algorithm for multifunctional phased array radar, Modern Radar, 10, 1, pp. 13-20, (2014)
  • [16] Hebert D.C., Radar resource management in a dense target environment, (2014)
  • [17] Zhao Y., Li J.X., Cao L.Y., Et al., Adaptive scheduling algorithm based on quadratic programming for multifunction phased array radars, Systems Engineering and Electronics, 34, 4, pp. 698-703, (2012)
  • [18] Zhang H.W., Xie J.W., Zhang Z.J., Et al., Scheduling algorithm over the hybird genetic particle swarm algorithm for the phased array radar, Systems Engineering and Electronics, 39, 9, pp. 1985-1991, (2017)
  • [19] Liang X., Huang M., Ning T., Et al., Hybrid algorithm of modern intellligent optimization and its application, (2014)
  • [20] Lei Y.J., Zhang S.W., Genetic algorithm toolbox and its application based on Matlab, (2014)
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