Adaptive Unscented Particle Filter Based on Predicted Residual
In order overcome the particle degradation and nonadjusted online in the traditional particle filter algorithm, an adaptive unscented particle filter algorithm based on predicted residua is proposed. The algorithm .adopts a new proposal distribution combing the unscented kalman filter with the adaptive factor. The algorithm uses Unscented Kalman filter to generate a proposal distribution, in which the covariance of the predicted measurement, the crosscovariance of the state and measurement and the covariance of the state update are online adjusted by predicted residual as adaptive factor. Simulation experiments results of nonlinear state estimation demonstrate that the adaptive unscented particle filter is more adaptive and accuracy is also unproved.
component Particle Filter Unscented Kalman Filter Adaptive Factor Predicted Residual
Hua-jian WANG Zhan-rong JING
School of Electronics and Information Engineering Northwestern Polytechnical University Xian Shanxi, China 710072
国际会议
重庆
英文
684-687
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）