Multisensor Distributed Weighted Kalman Filter Fusion With Network Delays, Stochastic Uncertainties, Autocorrelated, and Cross-Correlated Noises
针对一类带相关噪声和随机不确定性的不可靠网络系统，提出带有限长缓冲区的分布式加权卡尔曼滤波融合算法，能处理测量延迟或丢失，并具有较强容错能力。
This paper is concerned with the problem of distributed weighted Kalman filter fusion (DWKFF) for a class of multisensor unreliable networked systems (MUNSs) with correlated noises. The process noise and the measurement noises are assumed to be one-step, two-step cross-correlated, and one-step autocorrelated, and the measurement noises of each sensor are one-step cross-correlated. The stochastic uncertainties in the state and measurements are described by correlated multiplicative noises. The MUNSs suffer measurement delay or loss due to their unreliability. Buffers of finite length are proposed to deal with measurement delay or loss, and an optimal local Kalman filter estimator with a buffer of finite length is derived for each subsystem. Based on the new optimal local Kalman filter estimator, the DWKFF algorithm with finite length buffers has been developed which has stronger fault-tolerance ability. Simulation results illustrate the effectiveness of the proposed approaches.