Application of Adaptive Reduced Sigma Points Unscented Kalman Filter to the Tracking of Maneuvering Target |
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Authors: | ZHOU Zhan-xin and CHEN Jia-bin |
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Affiliation: | School of Information Science and Technology, Beijing institute of Technology, Beijing 100081, China;School of Information Science and Technology, Beijing institute of Technology, Beijing 100081, China |
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Abstract: | Based on the principle of statistical linear regression,a set of n 2 sigma points instead of 2n 1 sigma points used in the unscented Kalman filter (UKF),is constructed to approximate the system state.And filter accuracy is second order.Real-time of modified UKF is improved.In order to describe accurately the maneuvering target,the "current" statistical model is used.And the equation of acceleration error covariance is modified at every sample time of the filter.The modified adaptive UKF is presented for estimating the position,velocity and acceleration of maneuvering target.Monte Carlo simulations show the modified adaptive UKF acquires good performance for tracking position of maneuvering target.The modified adaptive UKF has better computational efficiency than UKF. |
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Keywords: | nonlinear filter adaptive UKF reduced sigma point maneuvering target tracking |
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