One crucial issue in particle filtering is the selection of proposal distribution. Good proposal can effectively alleviate particle degeneracy and thus improve filtering accuracy. In this paper, we propose a new type ...One crucial issue in particle filtering is the selection of proposal distribution. Good proposal can effectively alleviate particle degeneracy and thus improve filtering accuracy. In this paper, we propose a new type of proposal distribution for particle filter, called as R-IEKF proposal. By combining iterated extended kalman filter with Rauch-Tung-Striebel optimal smoother, the new proposal integrates the latest observation into system and approximates the true posterior distribution reasonably well, hence generating more precise and stable particles against measurement noise. The simulation results indicate that the improved particle filter with R-IEKF proposal prevails over PF-EKF and UPF both in tracking accuracy and filtering stability. Consequently, PF-RIEKF is a competitive choice in noisy measurement environment.展开更多
The alternate location method of a robot team is proposed. Three of the robots are kept still as beacon robots, not always the same ones, while the others are regarded as mobile robots. The mobile robots alternatively...The alternate location method of a robot team is proposed. Three of the robots are kept still as beacon robots, not always the same ones, while the others are regarded as mobile robots. The mobile robots alternatively measure the distance between one of them and three beacon robots with ultrasonic measurement module. The distance data are combined with its dead-reckoning information using iterated extended Kalrnan filter (IEKF) to realize the optimal estimate of its position. According to the condition the future beacon robots' positions should be desired ones, the target function and the nonlinear constrain equations are set up which are used by nonlinear optimization algorithm to estimate the position of the future beacon robots. By alternately changing the robots' roles as active beacon, the alternate location in unknown environment can'be realized. Process and result of the simulation test are given and the position estimation error is within + 10 ram, which proves the validity of this method.展开更多
基金Sponsored by the National Natural Science Foundation of China (Grant No. 61136002 )Key Project of Chinese Ministry of Education (Grant No.211180)Shannxi Provincial Industrial and Technological Project(Grant No. 2011K06-47)
摘要One crucial issue in particle filtering is the selection of proposal distribution. Good proposal can effectively alleviate particle degeneracy and thus improve filtering accuracy. In this paper, we propose a new type of proposal distribution for particle filter, called as R-IEKF proposal. By combining iterated extended kalman filter with Rauch-Tung-Striebel optimal smoother, the new proposal integrates the latest observation into system and approximates the true posterior distribution reasonably well, hence generating more precise and stable particles against measurement noise. The simulation results indicate that the improved particle filter with R-IEKF proposal prevails over PF-EKF and UPF both in tracking accuracy and filtering stability. Consequently, PF-RIEKF is a competitive choice in noisy measurement environment.
摘要The alternate location method of a robot team is proposed. Three of the robots are kept still as beacon robots, not always the same ones, while the others are regarded as mobile robots. The mobile robots alternatively measure the distance between one of them and three beacon robots with ultrasonic measurement module. The distance data are combined with its dead-reckoning information using iterated extended Kalrnan filter (IEKF) to realize the optimal estimate of its position. According to the condition the future beacon robots' positions should be desired ones, the target function and the nonlinear constrain equations are set up which are used by nonlinear optimization algorithm to estimate the position of the future beacon robots. By alternately changing the robots' roles as active beacon, the alternate location in unknown environment can'be realized. Process and result of the simulation test are given and the position estimation error is within + 10 ram, which proves the validity of this method.