【摘要】 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.更多还原

【Abstract】 In order to enhance the location estimation performance of mobile station(MS) tracking and positioning,a new method of mobile location optimal estimation based on the federated filtering structure and the simplified unscented Kalman filter(UKF) is presented.The proposed algorithm uses the Singer mobile statement model as the reference system,and the simplified UKF as the subfilters.The subfilters receive the two groups of independently detected time difference of arrival(TDOA) measurement inputs and Doppler measurement inputs,and produce local estimation outputs to the main estimator.Then the main estimator performs the optimal fusion of the local estimation outputs according to the scalar weighted rule,and a global optimal or suboptimal estimation result is achieved.Finally the main estimator gives feedback and reset information to the subfilters and the reference system for next step estimation.In the simulations,the estimation performance of the proposed algorithm is evaluated and compared with the simplified UKF method with TDOA or Doppler measurement alone.The simulation results demonstrate that the proposed algorithm can effectively reduce the location estimation error and variance of the MS,and has favorable performance in both root mean square error(RMSE) and mean error cumulative distribution function(CDF) .更多还原