本文選題：車輛定位 + 多傳感器融合　； 參考：《電子技術(shù)應(yīng)用》2017年06期

【摘要】：針對(duì)現(xiàn)有車輛定位裝置定位精度不高的問題,提出一種面向車輛自組織網(wǎng)絡(luò)的車輛精確定位方法。首先,獲取車輛上的多傳感器信息,融合這些信息構(gòu)建當(dāng)前車輛的狀態(tài)模型;然后,采用貝葉斯濾波方法計(jì)算車輛當(dāng)前狀態(tài)的可信度;接著,結(jié)合當(dāng)前車輛的一跳鄰居車輛信息估算其相對(duì)位置;最后,綜合上述信息修正車輛的當(dāng)前位置,提高車輛定位精度。實(shí)驗(yàn)表明,與常用的全球定位系統(tǒng)(GPS)、擴(kuò)展卡爾曼濾波方法相比,該方法的定位精度高,且受GPS定位誤差的影響小。
[Abstract]:In order to solve the problem that the positioning accuracy of existing vehicle positioning devices is not high, a vehicle positioning method for vehicle self-organizing network is proposed. Firstly, the multi-sensor information on the vehicle is obtained, and the state model of the current vehicle is constructed by fusion of the information. Then, the credibility of the current state of the vehicle is calculated by using Bayesian filtering method. Combined with the one-hop neighbor vehicle information of the current vehicle, the relative position of the vehicle is estimated. Finally, the vehicle location accuracy is improved by synthesizing the above information to correct the current position of the vehicle. The experimental results show that compared with the conventional GPS and extended Kalman filter, the proposed method has a high positioning accuracy and is less affected by the GPS positioning error.
【作者單位】： 江蘇開放大學(xué)信息與機(jī)電工程學(xué)院;南京信息工程大學(xué)電子與信息工程學(xué)院;
【分類號(hào)】：TN929.5;U495
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本文編號(hào)：1967974