非标准多传感器信息融合下的状态融合估计

给出了标准多传感器观测信息的统一融合模型,在此基础上分析了传感器观测系统参数对最优融合估计性能的影响.针对存在量测系统误差的非标准多传感器融合系统,构建了一种有效的系统误差参数估计模型.此外对传感器问具有不同非线性误差成份的融合系统,提出了一种基于互迭代自适应半参数的状态融合估计算法.该算法通过对非标准多传感器融合模型误差的补偿,利用线性和非线性迭代的方法来提取非线性因素,进而确定状态的最优融合估计.给出了应用该算法的具体步骤,并通过理论分析与仿真实验证明了该算法的有效性.

State optimal estimation with nonstandard multi-sensor information fusion

A unified linear fusion model of observation information with standard multi-sensor systems is presented,and then the relationship between the observation system parameters of sensors and the performance of optimal fusion estimation is analysed.For the nonstandard multi-sensor fusion system with observation system error,an effective observation system error parameters estimation model is established.Moreover for the fusion system with different nonlinear error factors among sensors,a new state fusion estimation algorithm based on alternant iterative and adaptive semiparametric regression model is introduced,which adopts frequency analysis to compensate the model error in nonstandard multi-sensor fusion system,and then extracts its nonlinear factor by the iteration with linear and nonlinear.The relevant steps to apply this algorithm are advanced.Finally,the method is validated with theory analysis and simulation experiment,and the comparative results show that the proposed algorithm is more effective.