考慮噪聲和初始狀態(tài)不確定性的車輛狀態(tài)UKF估計(jì)

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中圖分類號(hào)：U461.6 文獻(xiàn)標(biāo)識(shí)碼：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）

提升汽車主動(dòng)安全控制系統(tǒng)的性能是減少交通安全事故發(fā)生的關(guān)鍵因素，而準(zhǔn)確實(shí)時(shí)地獲取車輛狀態(tài)信息，又是主動(dòng)安全控制系統(tǒng)進(jìn)行控制決策的必要前提[。（剩余14303字）