基于非线性卡尔曼滤波的车辆定位优化算法

An Optimization Algorithm of Vehicle Positioning Based on Nonlinear Kalman Filter

智能交通系统(ITS)是未来交通系统发展的重要趋势,为了实现智能交通所提供的各种功能,必须获知车联网中车辆的准确位置。因此,如何快速准确地实现车辆定位是现代智能交通系统所要研究的一个重要问题。实际系统中一般都是非线性系统,所以需要采用非线性的卡尔曼滤波算法。文中采用了非线性无迹卡尔曼滤波算法。无迹卡尔曼滤波在车辆运动状态发生突变时,车辆定位精度有所下降。为了提高无迹卡尔曼滤波算法在车辆运动状态发生突变时的定位精度,文中将自适应的交互多模算法和无迹卡尔曼滤波算法相结合,进一步提高了车辆的定位精度,同时也更能适应车辆的各种机动运动状态。仿真实验结果表明,交互多模无迹卡尔曼滤波算法的定位精度相较于无迹卡尔曼滤波算法有显著提升。

Intelligent Transportation Systems （ITS） is an important trend in the development of future transport systems. In order to provide the various functions ,the system should acquire the exact position of the vehicle. How to achieve accurate and rapid vehicle position is an important issue which modern intelligent transportation systems must go to research. The actual systems are generally nonlinear system, so a nonlinear unscented Kalman filter algorithm is used. When the vehicle is in motion is mutated, the accuracy of unscented Kalman filter algorithm is declined. Due to improving the accuracy of vehicle position while the vehicle is motor-driven, the interacting mul- tiple model algorithm is combined with the unscented Kalman filtering. At the same time, the improved algorithm can adapt to a variety of motion state of the vehicle. Simulation results show that the positioning accuracy of interacting multiple model unscented Kalman filtering algorithm is obviously better than unscented Kalman filtering algorithm.