4.7 Article

Multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter

Journal

MEASUREMENT
Volume 176, Issue -, Pages -

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.109139

Keywords

Strong tracking theory; Multiple fading factors; Noise covariance matrix; Variational Bayesian; Adaptive Kalman filter

Funding

  1. National Natural Science Foundation of China [41974026, 41874006]
  2. China University of Mining and Technology [2020WLJCRCZL052]
  3. Jiangsu Education Department [KYCX20_2058]

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A multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed to address the issue of inaccurate system models or statistical characteristics of noise affecting state estimation accuracy. The algorithm models measurement noise covariance matrix using inverse Wishart distribution and constructs scalar fading factor with remodified measurement noise covariance matrix and innovation covariance matrix estimated by exponential weighting method. Multiple fading factors are calculated to correct predicted error covariance matrix, leading to improved tracking ability compared to existing filters in target tracking simulations.
If the system model or the statistical characteristics of noise are inaccurate, the past measurements will directly affect the accuracy of current state estimation or even lead to filtering divergence. To overcome above difficulties, a multiple fading factors-based strong tracking variational Bayesian adaptive Kalman filter is proposed. Firstly, the inverse Wishart distribution is adopted to model the measurement noise covariance matrix. Secondly, the remodified measurement noise covariance matrix and the innovation covariance matrix estimated by exponential weighting method are employed to construct the scalar fading factor. Next, the multiple fading factors are calculated to correct the predicted error covariance matrix. Finally, the local optimal estimations of measurement noise covariance matrix and state are obtained by variational Bayesian approach. The target tracking simulations verify that the proposed algorithm has better tracking ability for the predicted error covariance matrix and the measurement noise covariance matrix compared with the existing filters.

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