江苏大学学报（自然科学版）2026，Vol.47Issue(3)：267-274,291,9.DOI:10.3969/j.issn.1671-7775.2026.03.003
基于改进人工鱼群粒子滤波算法的车辆状态估计
Vehicle state estimation based on improved artificial fish swarm particle filtering algorithm
摘要
Abstract
To solve the problem of estimation accuracy decrease or even divergence due to the degradation of particle weights in vehicle state estimation by particle filtering(PF),the improved artificial fish swarm algorithm-particle filter(AFSA-PF)vehicle state estimation method was proposed.To improve the global search capability of AFSA and reduce the risk of falling into local extrema,the attenuation function was introduced to dynamically adjust the field of view to achieve global search and local search using larger and smaller fields of view in the early and late stages of the iteration,respectively.The random step strategy in the movement of artificial fish was changed,and the adaptive step size was used to achieve dynamic switching between large and small step sizes in different situations.The foraging and clustering behaviors of the above improved AFSA were used to optimize the particle weight calculation and particle set resampling in PF state estimation.The improved algorithm was verified by the joint simulation of Carsim and Simulink.The results show that compared with AFSA-PF,by the improved AFSA-PF,the MAE and RMSE of the yaw rate estimation are reduced by respective 40.1％and 34.9％under the dual-line shifting condition,and the MAE and RMSE of the sideslip angle estimation are reduced by respective 35.1％and 33.5％.Under the step conditions,the MAE and RMSE of the estimated yaw rate are reduced by respective 52.7％and 36.3％,and the MAE and RMSE of the estimated sideslip angle are reduced by respective 51.5％and 24.0％.关键词
智能汽车控制系统/车辆状态估计/粒子滤波/人工鱼群算法/衰减函数/自适应步长Key words
intelligent vehicle control system/vehicle state estimation/particle filtering/artificial fish swarm algorithm/attenuation function/adaptive step size分类
交通工程引用本文复制引用
刘文光,蒋祝安,何仁,丁贝,车华军..基于改进人工鱼群粒子滤波算法的车辆状态估计[J].江苏大学学报（自然科学版）,2026,47(3):267-274,291,9.基金项目
江苏省科技计划项目(BE2023074) （BE2023074）
