摘要
车辆定位精度与采用的动力学模型直接相关。文章针对单一的动力学模型难以精确描述车辆行驶过程中的运动状态变化,提出复合CV模型和CSM模型,引入自适应切换机制,设计一种车辆定位跟踪算法。通过与CSM模型实验结果进行比较,验证了该算法的优越性。
The accuracy of vehicle positioning is directly affected by the dynamic models. It is difficult to use a single model to describe vehicle movement precisely because of the variable motion in the running process of vehicles. By integrating the constant velocity（CV） model and the current statistical model（CSM） and introducing the adaptive switching mechanism, an algorithm for vehicle positioning and tracking is designed. The computation results are compared with those by the CSM, which verifies the effectiveness of the algorithm.
出处
《合肥工业大学学报（自然科学版）》
CAS
CSCD
北大核心
2009年第11期1645-1647,共3页
Journal of Hefei University of Technology：Natural Science
基金
中兴通讯研究基金资助项目(ZXJH20041221-0056)
广东省教育部产学研结合资助项目(2009B090300303)
安徽省科技攻关资助项目(07010202045)
关键词
车辆定位
动态滤波
卡尔曼滤波
“当前”统计模型
vehicle positioning
dynamic filtering
Kalman filtering
current statistical model（CSM）
