基于扩展卡尔曼滤波的动态协同定位算法

A Research of Dynamic Cooperative Localization System via Extended Kalman Filter

卫星导航系统是目前应用最为广泛的一种定位与导航系统,然而在城区、峡谷、森林等复杂条件下其所提供的定位与导航性能会下降甚至无法提供服务。针对该问题,已经有多种卫星导航增强手段被提出,其中,用户间的协同定位由于可以减轻对基础设施的依赖,具有很大发展前景。本文提出了基于扩展卡尔曼的协同定位算法,建立了动态条件下用户的位置速度矢量模型的系统方程,利用该算法在动态协同定位系统中实现对用户的位置和速度的估计。文中论述了算法的详细过程,并通过计算机软件实时产生卫星的三维位置,构建了一个动态用户协同定位场景。数值仿真结果表明,该算法能够有效地估计出用户的位置和速度。

The global navigation satellite system（GNSS）is widely used in navigation and positioning system.While in some GNSS-challenged environments,such as urban canyons,dense foliage and building block,it will fail to guarantee the service quality or can＇t provide service at all.Several satellite enhanced means were presented to solve such problem and among them,the cooperative localization has great prospective as it can reduce the dependence of infrastructure.This paper established an equation of the system based on a position-velocity-time（PVT）model and employed the extended Kalman filter algorithm to estimate the user＇s location and velocity in a dynamic cooperative localization system.This paper gave out the detailed derivation process and built the model though the three-dimension position of each satellite generated by software.The simulation results show that the algorithm can track the path of dynamic users and effectively estimate the user＇s velocity.