考虑噪声和初始状态不确定性的车辆状态UKF估计

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中图分类号：U461.6 文献标识码：A DOI： 10.3969/j.issn.1674-8484.2025.03.007

Abstract：An improved unscented Kalman filter （UKF） vehicle state estimation method was proposed to improve the estimationaccuracyof vehicle states in thepresence of noise covariance matrixand initial state uncertainties.This method introduced a windowing process based on the maximum a posteriori （MAP） estimationstrategy toachievedynamicestimationof thenoisecovariancematrix，whilealsointegratingastatic particlefiter（SPF）algorithm toestimate the initial vehiclestates.The improved UKF'sestimationaccuracy was verified usingaco-simulationplatformwith CarSimand MATLAB/Simulink.Theresults show that，when measurement noise deviates from the truevalue，the windowed MAPdynamic estimation method for the noisecovariance matrix improves the estimationaccuracy of longitudinal and lateral speeds by 90% and 80% respectively，compared to thestandard UKF.Incomparison to the UKFwithadaptive noise covariance matrix adjustment， the estimation accuracy increases by 75% and 56% ，respectively. Under initial state uncertainty， the SPF method improves the estimation accuracy of longitudinal and lateral vehicle speeds by 94% and 90% respectively.Therefore，the proposed improved UKF estimation method significantly enhances estimation accuracyand robustness in the presence of noise covariance matrixand initial state uncertainties.

Keywords：electric vehicle; vehicle state estimation; unscented Kalman fiter （UKF）; maximuma posteriori （MAP）; static particle filter （SPF）

提升汽车主动安全控制系统的性能是减少交通安全事故发生的关键因素，而准确实时地获取车辆状态信息，又是主动安全控制系统进行控制决策的必要前提[。（剩余14303字）