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基于模型自適應(yīng)粒子濾波的汽車狀態(tài)估計(jì)
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國(guó)家自然科學(xué)基金資助項(xiàng)目(61263031 )和江蘇省大型工程裝備檢測(cè)與控制重點(diǎn)建設(shè)實(shí)驗(yàn)室重點(diǎn)資助項(xiàng)目(JSKLEDC201202)


Estimation of Vehicle States Based on Adaptive Model Particle Filter
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    摘要:

    為準(zhǔn)確實(shí)時(shí)獲取汽車行駛過(guò)程中的狀態(tài)變量,,提出了一種模型自適應(yīng)更新粒子濾波方法。建立了非高斯噪聲和非線性輪胎的汽車動(dòng)力學(xué)模型,,并基于小波變換的方法,采用高頻子帶估計(jì)傳感器量測(cè)噪聲的實(shí)時(shí)方差,,提高了觀測(cè)似然函數(shù)的真實(shí)擬合程度,,結(jié)合自適應(yīng)自回歸模型對(duì)整車系統(tǒng)的狀態(tài)進(jìn)行自適應(yīng)更新,較好地克服了粒子權(quán)值的退化現(xiàn)象,;基于ADAMS/Car的虛擬實(shí)驗(yàn)和實(shí)車實(shí)驗(yàn)驗(yàn)證了所提方法的有效性,。實(shí)驗(yàn)結(jié)果表明該方法在估計(jì)精度和克服噪聲方面均優(yōu)于常用方法,滿足汽車狀態(tài)估計(jì)器的軟件性能要求。

    Abstract:

    In order to get the accurate and real-time vehicle state variables in running, a new kind of model adaptive update particle filter method is proposed. The non-Gaussian and non-linear tire noise vehicle dynamics model is established. High frequency sub-band is used to estimate real-time measurement noise variance of sensors based on the wavelet transform. The real fitting degree of observation likelihood function is improved and the degradation phenomenon of particle weight is improved to a certain extent by the combination of the adaptive auto regression model of the whole vehicle system state. Virtual experiment based on ADAMS/Car and real vehicle experiment verify the validity of the proposed method. Experiment results show that the estimation precision and anti-noise performance of the proposed method are superior to those of the commonly used method, and can satisfy the requirements of vehicle state estimation.

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秦錄芳,李偉,李軍,曹潔.基于模型自適應(yīng)粒子濾波的汽車狀態(tài)估計(jì)[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2014,45(10):22-28. Qin Lufang, Li Wei, Li Jun, Cao Jie. Estimation of Vehicle States Based on Adaptive Model Particle Filter[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(10):22-28.

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  • 收稿日期:2014-05-29
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  • 在線發(fā)布日期: 2014-10-10
  • 出版日期: 2014-10-10
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