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研究具有传感器增益退化、模型不确定性的多传感器融合估计问题,其中传感器增益退化现象描述为统计特性已知的随机变量,模型的不确定性描述为系统矩阵受到随机扰动.设计一种局部无偏估计器结构,并建立以局部估计器增益为决策变量、以有限时域下融合估计误差为代价函数的优化问题.在给出标量融合权重时,考虑到求得最优的局部估计器增益的解析形式较为困难,通过最小化代价函数的上界得到一组次优的局部估计器增益.最后通过算例仿真表明了所设计融合估计器的有效性.
The problem of multi-sensor fusion estimation with sensor gain degradation and model uncertainty is studied, in which the sensor gain degradation is described as a stochastic variable with known statistical properties. The uncertainty of the model is described as a random perturbation of the system matrix. Partial estimator structure and the optimization problem of taking the local estimator gain as the decision variable and taking the fusion estimation error as the cost function in the finite time domain is established. When the scalar fusion weight is given, the optimal local estimator gain It is more difficult to obtain a set of suboptimal local estimator gain by minimizing the upper bound of the cost function.Finally, the simulation results show that the proposed fusion estimator is effective.