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Research Papers

Interactive Multiple Model Filter for Tracking a Pursuer With Proportional Navigation Guidance Law

[+] Author and Article Information
Runle Du

National Key Laboratory of Science
and Technology on Test Physics
and Numerical Mathematics,
Beijing 100076, China
e-mail: jenniferdu@126.com

Xinguang Zou

School of Electrical Engineering and Automation,
Harbin Institute of Technology,
Harbin 150001, China
e-mail: xgzou@hit.edu.cn

Di Zhou

School of Astronautics,
Harbin Institute of Technology,
Harbin 150001, China
e-mail: zhoud@hit.edu.cn

Jiaqi Liu

National Key Laboratory of Science
and Technology on Test Physics
and Numerical Mathematics,
Beijing 100076, China
e-mail: ljq006@vip.sina.com

1Corresponding authors.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received January 26, 2017; final manuscript received December 13, 2017; published online March 7, 2018. Assoc. Editor: Soo Jeon.

J. Dyn. Sys., Meas., Control 140(8), 081003 (Mar 07, 2018) (9 pages) Paper No: DS-17-1049; doi: 10.1115/1.4039156 History: Received January 26, 2017; Revised December 13, 2017

This paper addresses a pursuer tracking problem where the pursuer's acceleration is given by a proportional navigation (PN) guidance law with a time-varying navigation ratio which varies with the relative range between the pursuer and its target. Based on a motion model that exactly describes the relative motion and the PN guidance law, a novel filter for tracking such a pursuer is designed using interactive multiple model (IMM) algorithm and unscented Kalman filtering (UKF) technique. This filter is able to accurately estimate the relative range, relative velocity, and the acceleration of pursuer even if the pursuer adopts a PN guidance law with time-varying navigation ratio. The proposed tracking method is evaluated in extensive Monte Carlo simulations. It is shown that accurate estimation results have been obtained, and the model probabilities in the IMM UKF filter are consistent with real situations.

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References

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Figures

Grahic Jump Location
Fig. 1

Engagement between pursuer and evader

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Fig. 2

Estimation of the pursuer acceleration's projections in the scene inertial coordinate

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Fig. 3

Estimation of relative velocity projections in the scene inertial coordinate

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Fig. 13

RMSEs comparison of ry,vy,apy between IMM_11PN6 and UKF_PN7

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Fig. 14

RMSEs comparison of the navigation ratio between IMM_11PN6 and UKF_PN7

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Fig. 15

Comparison of the mean of the estimation on navigation ratio between IMM_11PN6 and UKF_PN7

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Fig. 4

Estimation of the LOS angular rate

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Fig. 5

Probabilities of IMM_3PN6 models

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Fig. 6

RMSEs of rx,vx,apx of IMM_3PN6

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Fig. 7

RMSEs of ry,vy,apy of IMM_3PN6

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Fig. 8

RMSEs comparison of rx,vx,apx between IMM_3PN6 and UKF_PN7

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Fig. 9

RMSEs comparison of ry,vy,apy between IMM_3PN6 and UKF_PN7

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Fig. 10

RMSEs comparison of the navigation ratio between IMM_3PN6 and UKF_PN7

Grahic Jump Location
Fig. 11

Comparison of the mean of the estimation on navigation ratio between IMM_3PN6 and UKF_PN7

Grahic Jump Location
Fig. 12

RMSEs comparison of rx,vx,apx between IMM_11PN6 and UKF_PN7

Tables

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