An Improved Particle Filter Based on UKF and Weight Optimization
- Resource Type
- Conference
- Authors
- Hui, Zhao; Lifen, Wang; Yuan, Ren; Mengmeng, Geng
- Source
- 2020 IEEE 3rd International Conference on Information Communication and Signal Processing (ICICSP) Information Communication and Signal Processing (ICICSP), 2020 IEEE 3rd International Conference on. :80-83 Sep, 2020
- Subject
- Communication, Networking and Broadcast Technologies
Particle filters
Filtering
Filtering algorithms
Kalman filters
Mathematical model
Degradation
Prediction algorithms
particle filtering
state estimation
weight optimization
suggested distribution function
particle depletion
- Language
Aiming at the problem of limited efficiency and accuracy of state estimation in the case of non-linear and non-Gaussian systems, this paper proposes an improved particle filtering algorithm based on edge unscented Kalman filtering and weight optimization for the existing efficiency problems of UPF. Compared with traditional particle filtering, the improved filtering algorithm generates a suggested distribution function in order to avoid excessive variance of particle weights and combines the latest observation information to calculate a more efficient edgeless trace Kalman filter; during the resampling process The weight-optimized resampling method is introduced to solve the problem of particle depletion and improve particle diversity. It can be verified through theoretical derivation and simulation analysis that the improved algorithm effectively improves the calculation efficiency and has better estimation accuracy.