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为了进一步提高移动台的跟踪和定位性能,提出了一种基于联邦滤波结构和简化UKF的移动位置最优估计与融合新方法.该算法以Singer移动台运动模型作为参考系统,以简化UKF滤波器作为子滤波器,对2组独立检测的TDOA和Doppler测量值进行局部估计;然后在主滤波器中,对子滤波器的估计结果按标量加权进行最优融合,得到全局最优或次最优融合估计结果;最后主滤波器利用全局估计结果对子滤波器和参考系统进行反馈和信息重置,以进行下一步估计.仿真试验中,对该算法用于移动台位置估计的效果和性能进行评估,并与基于TDOA和基于Doppler的简化UKF方法做比较.仿真结果表明,该算法能有效降低移动台位置估计的误差和方差,具有良好的均方根误差和均值误差CDF性能.
In order to further improve the tracking and localization performance of mobile stations, a new method for the optimal estimation and fusion of mobile positions based on federated filtering structure and simplified UKF is proposed. The algorithm takes Singer mobile station motion model as a reference system to simplify the UKF filter As a sub-filter, TDOA and Doppler measurements of two independent sets of TDOA and Doppler measurements are estimated locally. Then in the main filter, the results of the sub-filters are optimally fused by scalar weighting to obtain the global optimal or sub-optimal Finally, the main filter uses the global estimation results to feedback and reset the sub-filter and reference system for the next step estimation.In the simulation experiment, the performance and performance of the algorithm for the mobile station position estimation And compared with simplified UKF method based on TDOA and Doppler based on Doppler.The simulation results show that this algorithm can effectively reduce the error and variance of mobile station position estimation and has good root mean square error and mean error CDF performance.