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A practical adaptive nonlinear tracking algorithm with range rate measurement

Author

Listed:
  • Haiyan Yang
  • Hongqiang Liu
  • Zhongliang Zhou
  • An Xu

Abstract

It is difficult to answer the problem whether the range rate measurement should be adopted to track a target in a tracking scenario. A practical adaptive nonlinear tracking algorithm with the range rate measurement is proposed, which avoids this problem and achieves good accuracy of target state estimation. First, three popular nonlinear filtering algorithms only with the position measurement are surveyed. Second, three popular nonlinear filtering algorithms with the position and range rate measurements are surveyed. Then, a novel tracking algorithm with range rate measurement is proposed based on the cumulative sum detector and the above two kinds of nonlinear algorithms. The results of simulation experiment demonstrate that the range rate measurement could reduce accuracy of the target state estimation in mismatch tracking scenarios. The results of simulation experiment also verify that the performance of proposed algorithm is better than the current state and the art interacting multiple-model algorithm and can well follow the state estimation output of the measurement equation matching the tracking scenario.

Suggested Citation

  • Haiyan Yang & Hongqiang Liu & Zhongliang Zhou & An Xu, 2018. "A practical adaptive nonlinear tracking algorithm with range rate measurement," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    • Handle: RePEc:sae:intdis:v:14:y:2018:i:5:p:1550147718776863
    DOI: 10.1177/1550147718776863
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    References listed on IDEAS

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    1. Cho, Haeran & Fryzlewicz, Piotr, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," LSE Research Online Documents on Economics 57147, London School of Economics and Political Science, LSE Library.
    2. Haeran Cho & Piotr Fryzlewicz, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 475-507, March.
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