When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.
为了有效提高三维空间中无线传感器网络节点定位算法的效率,提出了一种基于虚拟分层的节点模糊信息定位方法(Nodes’Fuzzy Information Localization algorithm on Virtual Stratification,NFIL-VS),该方法在定位前对网络实现虚拟分层,分层后测量各平面上节点之间的方向角和俯仰角等模糊信息实现节点定位。每一轮定位结束后,判断并更新无效锚节点的位置。网络中的节点被定位后充当二级锚节点辅助定位其他节点。通过实验仿真,与SNLSFAMC算法和MANLFI算法相比,提出的NFIL-VS算法提高了节点定位精度,降低了网络能耗。
