非标准多传感器信息融合下的状态融合估计

State optimal estimation with nonstandard multi-sensor information fusion

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。此外对传感器间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。

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 sys- tem 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 nonlin- ear factor by the iteration with linear and nonlinear. The relevant steps to apply this algorithm are advanced. Fi- nally, the method is validated with theory analysis and simulation experiment, and the comparative results show that the proposed algorithm is more effective.