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Iterative Unbiased Conversion Measurement Kalman Filter with Interactive Multi-model Algorithm for Target Tracking

  • Da LiEmail author
  • Xiangyu Zou
  • Ping Lou
  • Ruifang Li
  • Qin Wei
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 946)

Abstract

For modern tracking systems, the tracking target generally has the characteristics of high speed and mobility. Tracking targets has always been a challenging problem, especially tracking high speed and strong maneuvering targets, which is difficult in theory and practice. An interactive multi-model (IMM) based on iterative unbiased conversion measurement Kalman filter (IUCMKF) is proposed. The new algorithm takes advantages of the interactive and complementary characteristics between different models to overcome the problems of low precision and filter divergence. First, investigate the function of conversion measurement Kalman filter (CMKF), debiased conversion measurement Kalman filter (DCMKF), and IUCMKF on double-model and multiple-model. Secondly, compare and analyze the performance of the three algorithms (CMKF-IMM, DCMKF-IMM and IUCMKF-IMM). Finally, identify the effect and impact of the combination of the four different models including CA, Singer, CS, and Jerk on the accuracy of target tracking. The results of numerical simulation show the choice of the number and type of models should be weighed according to the actual simulation environment. Even though more choices of models can improve the tracking accuracy of the target, but that also greatly increases the complexity of the algorithm and the error consistency of the algorithm also cannot be guaranteed to some extent. Therefore, compared with CMKF-IMM and DCMKF-IMM, the new algorithm can attain a more accurate state of the target being tracked and the covariance estimation. It also has more potential in improving tracking accuracy.

Keywords

Target tracking Interactive multi-model Iterative unbiased conversion measurement Kalman filter 

Notes

Acknowledgment

This work is partly funded by the National Natural Science Foundation of China (Grant No. 51475347).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Da Li
    • 1
    • 2
    Email author
  • Xiangyu Zou
    • 1
    • 2
  • Ping Lou
    • 1
    • 2
  • Ruifang Li
    • 1
    • 2
  • Qin Wei
    • 1
    • 2
  1. 1.School of Information EngineeringWuhan University of TechnologyWuhanChina
  2. 2.Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of EducationWuhan University of TechnologyWuhanChina

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