双重未知干扰解耦的多传感器系统偏差校正与状态估计

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.