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Sensors 2019, 19(6), 1371; https://doi.org/10.3390/s19061371

Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance

1
School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
2
Science and Technology on Aircraft Control Laboratory, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 13 February 2019 / Revised: 13 March 2019 / Accepted: 17 March 2019 / Published: 19 March 2019
(This article belongs to the Special Issue Multiple Object Tracking: Making Sense of the Sensors)
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Abstract

The unscented Kalman filter (UKF) is widely used to address the nonlinear problems in target tracking. However, this standard UKF shows unstable performance whenever the noise covariance mismatches. Furthermore, in consideration of the deficiencies of the current adaptive UKF algorithm, this paper proposes a new adaptive UKF scheme for the time-varying noise covariance problems. First of all, the cross-correlation between the innovation and residual sequences is given and proven. On this basis, a linear matrix equation deduced from the innovation and residual sequences is applied to resolve the process noise covariance in real time. Using the redundant measurements, an improved measurement-based adaptive Kalman filtering algorithm is applied to estimate the measurement noise covariance, which is entirely immune to the state estimation. The results of the simulation indicate that under the condition of time-varying noise covariances, the proposed adaptive UKF outperforms the standard UKF and the current adaptive UKF algorithm, hence improving tracking accuracy and stability. View Full-Text
Keywords: adaptive filtering; data fusion; target tracking; non-linear filtering; unknown noise statistics adaptive filtering; data fusion; target tracking; non-linear filtering; unknown noise statistics
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ge, B.; Zhang, H.; Jiang, L.; Li, Z.; Butt, M.M. Adaptive Unscented Kalman Filter for Target Tracking with Unknown Time-Varying Noise Covariance. Sensors 2019, 19, 1371.

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