Open AccessAdaptive particle filter for state estimation with application to non‐linear system
Author(s) -
Zhao Fangfang,
Cai Ruijie
Publication year - 2022
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12147
Subject(s) - resampling , particle filter , computer science , kalman filter , adaptive filter , algorithm , auxiliary particle filter , ensemble kalman filter , kernel adaptive filter , extended kalman filter , simplex algorithm , perspective (graphical) , filter (signal processing) , invariant extended kalman filter , filter design , control theory (sociology) , mathematical optimization , mathematics , artificial intelligence , linear programming , computer vision , control (management)
Particle filtering (PF) has certain application value, but the disadvantage is that there is a phenomenon of particle degradation. In order to reduce the impact of this problem, this paper presents a new adaptive PF approach to improve the estimate accuracy. From the perspective of selecting an appropriate important density functions, in this filter, the particles are first updated using the Spherical Simplex Unscented Kalman Filter algorithm, and then the particles are updated using the Adaptive Extended Kalman filter algorithm. Simultaneously, from the perspective of improving the resampling method, a new resampling technique based on the random resampling method has been designed and fused to this filter. The comparison and analysis of two simulation schemes have been conducted to assess the performance of the designed filtering algorithm. The simulation results show the effectiveness of the proposed approach.