Adaptive fading factor unscented Kalman filter with application to target tracking
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REVIEW
Adaptive fading factor unscented Kalman filter with application to target tracking Peng Gu1,2 · Zhongliang Jing1 · Liangbin Wu2 Received: 6 July 2020 / Revised: 16 October 2020 / Accepted: 23 October 2020 © The Author(s) 2020
Abstract One purpose of target tracking is to estimate the states of targets, and unscented Kalman filter is one of the effective algorithms for estimating in the nonlinear tracking problem. Considering the characteristics of complex maneuverability, it is easy to reduce the tracking accuracy and cause divergence due to the mismatch between the system model and the practical target motion model. Adaptive fading factor is an effective counter to this problem, having been instrumental in solving accuracy and divergence problems. Fading factor can adaptively adjust covariance matrix online to compensate model mismatch error. Moreover, fading factor not only improves the filtering accuracy, but also automatically adjusts the error covariance in response to the different situation. The simulation results show that the adaptive fading factor unscented Kalman filter has more advantages in target tracking and it can be better applied to nonlinear target tracking. Keywords Adaptive fading factor · Target tracking · UKF · Accuracy
1 Introduction Kalman filter algorithm can derive the optimal estimation of state under conditions involving linear-Gaussian assumption, which is based on the known system model, observation model and statistics of noises. When they are inconsistent with target behavior model, the estimation error will increase. Singer model can express the acceleration of maneuver as a time-dependent process [1], which conforms to actual target motion. By adjusting the frequency coefficient of maneuvering to achieve better tracking effect, it has been widely used for nonlinear system [2, 3]. The “current” statistical model was proposed as a representative of adaptive tracking algorithm [4]. In this model, the acceleration noise is assumed to
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Peng Gu [email protected] Zhongliang Jing [email protected] Liangbin Wu [email protected]
1
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai, China
2
AVIC Leihua Electronic Technology Research Institute, Wuxi, China
be Rayleigh distribution, and the distribution of state noise is updated in real time through mean value of acceleration. In the process of target tracking, if the system model deviates from their actual values by unknown random bias, the virtual noise is usually used to reduce confidence level of the filter to the system model, but it is difficult to determine how much virtual noise to be added. At this time, we should consider abandoning the previous observation data, and pay more attention to the newly generated observation value, so that the filter has better performance. Researchers have proposed a variety of solutions for this problem. The adaptive fading Kalman filter (AFKF) [13, 14] uses the fading factor to suppress the memory length of the filter, so as to make full use of the new measu
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