双重未知干扰解耦的多传感器系统偏差校正与状态估计

具有未知干扰输入的随机系统状态估计问题广泛存在于控制、通信、信号处理和故障诊断等领域，但目前的研究成果大多局限于单传感器动态离散系统。针对状态方程含有未知干扰、量测方程含有未知偏差的多传感器系统状态估计问题开展了研究，提出了一种双重未知干扰解耦下的最小方差无偏估计滤波器。首先，建立量测偏差通用演化模型；然后，解耦偏差演化模型中的未知输入，设计最小方差无偏估计器对量测干扰偏差进行估计；之后，利用估计出的量测偏差来校正动态系统测量值；最后，根据量测偏差校正后的系统模型设计最优状态观测器，获得具有最小方差无偏的状态估计。径向飞行控制系统的例子验证了所提方法的有效性，与相关方法的仿真对比反应了所提方法的优越性。

Dual unknown interference decoupled multi-sensors bias compensation and state estimate

Stochastic system state estimate subjects to the unknown interference input widely exists in many fields, such as control, communication, signal processing, and fault diagnosis. However, the current research is mostly limited to the single sensor dynamic discrete system. This paper examines the state estimate of multi-sensors system in which the state equation contains the unknown interference and the measurement equation contains the unknown bias, proposing a dual interference decoupled minimum variance unbiased estimator. Firstly, the general evolution model of measurement bias is established. Then, the unknown input is decoupled from the measurement bias evolution model. After that, the estimated measurement bias is utilized to compensate the dynamic system measurement. Finally, the optimal state observer is designed based on the compensated system measurement model, and the state estimate with minimum variance is obtained. Simulation results of the radial flight controller verified the effectiveness of the proposed method. Comparing with simulated results of the relative methods, the proposed algorithm shows its superiority.