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Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (20): 629825.doi: 10.7527/S1000-6893.2024.29825

• Aeronautics Computing and Simulation Technique • Previous Articles    

A Fisher information based adaptive filtering algorithm for sensor trajectory planning

Yumei HU1, Quan PAN2,3, Bao DENG1()   

  1. 1.AVIC Xi’an Aeronautics Computing Technique Research Institute,Xi’an 710065,China
    2.School of Automation,Northwestern Polytechnical University,Xi’an 710072,China
    3.Key Laboratory of Information Fusion Technology,Ministry of Education,Northwestern Polytechnical University,Xi’an 710072,China
  • Received:2023-11-02 Revised:2023-11-28 Accepted:2024-02-19 Online:2024-03-12 Published:2024-03-11
  • Contact: Bao DENG E-mail:dengbao15@sina.com
  • Supported by:
    National Natural Science Foundation of China(61976080)

Abstract:

To address the problem of incomplete observability of the bearing-only tracking system with single sensor, an optimal motion trajectory of the sensor is presented under the condition of maximizing variational evidence lower bound to accumulate more information from measurement. Firstly, assuming that sensor’s speed is a constant, a joint optimization framework with the super-parameters of sensor’s heading, state estimation and its estimation error covariance is established by using variational Bayesian inference on this basis, the Fisher information matrix with respect to sensor’s heading is derived by maximizing the evidence lower bound, so that the optimal heading can be computed at the point of Fisher information accumulation maximization. Meanwhile, considering the robustness of estimated system, a weighted Kullback-Leibler divergence between variational distribution and the posteriori is taken as a regularization to constructed an optimization function, in which state estimation and the associated error covariance are set as variational super-parameters. Furthermore, the partial derivatives of optimization function with respect to state estimation and the associated error covariance are presented to compute estimation update. Simulations of bearing-only tracking system are presented showing that the proposed algorithm can accumulate information from measurement effectively and achieve a higher estimation performance of accuracy by changing sensor’s heading.

Key words: sensor trajectory planning, variational Bayesian inference, nonlinear filtering, adaptive filtering, Fisher information matrix

CLC Number: