Journal
SENSORS
Volume 19, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/s19061371
Keywords
adaptive filtering; data fusion; target tracking; non-linear filtering; unknown noise statistics
Funding
- National Key Research and Development Program of China [2017YFC0821102, 2016YFB0502004]
- Beijing Intelligent Logistics System Collaborative Innovation Center
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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.
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