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Tracking Air-to-Air Missile Using Proportional Navigation Model with Genetic Algorithm Particle Filter

Author

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  • Hongqiang Liu
  • Lei Yu
  • Chenwei Ruan
  • Zhongliang Zhou

Abstract

The purpose of this paper is to track the air-to-air missile. Here we put forward the PN-GAPF (Proportional Navigation motion model and Genetic Algorithm Particle Filter) method to solve the problem. The main jobs we have done can be listed as follows: firstly, we establish the missile state space model named as the Proportional Navigation (PN) motion model to simulate the real motion of the air-to-air missile; secondly, the PN-EKF and PN-PF methods are proposed to track the missile, through combining PN motion model with EKF and PF; thirdly, in order to solve the particle degeneracy and diversity loss, we introduce the intercross and variation in GA to the particles resampling step and then the PN-GAPF method is put forward. The simulation results show that the PN motion model is better than the CV and CA motion models for tracking the air-to-air missile and that the PN-GAPF method is more efficient than the PN-EKF and PN-PF.

Suggested Citation

  • Hongqiang Liu & Lei Yu & Chenwei Ruan & Zhongliang Zhou, 2016. "Tracking Air-to-Air Missile Using Proportional Navigation Model with Genetic Algorithm Particle Filter," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, March.
    • Handle: RePEc:hin:jnlmpe:3921608
    DOI: 10.1155/2016/3921608
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