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.
Highlights
The main mission of target tracking is to estimate the dynamic parameters and show the trajectory of a maneuvering target by extracting the useful information from sensor observations [1]
The effectiveness of the proposed adaptive unscented Kalman filter (UKF) algorithm for maneuvering target tracking will be illustrated through the simulations of different cases
The covariance of the process and measurement noise is tuned in real time by using the innovation, residual and redundant measurement sequences
Summary
The main mission of target tracking is to estimate the dynamic parameters and show the trajectory of a maneuvering target by extracting the useful information from sensor observations [1]. The statistical properties of measurement noise can be obtained in advance from the tracking sensor’s physical characteristics, these aren’t reliable, since they are affected by the external interference, especially in complicated environments. It is difficult to obtain the system noise with an accurate statistical covariance because of the random characteristics of acceleration and external manipulation [8]. The time-varying noise covariances involved in the maneuvering target tracking system make the traditional non-adaptive filtering algorithms no longer suitable. An adaptive and stable filtering algorithm with high performance is required to deal with the noise covariance uncertainty
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