Optimal distributed Kalman filtering fusion for multirate multisensor dynamic systems with correlated noise and unreliable measurements

Abstract

An optimal distributed fusion estimation problem is concerned in this study for a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements. The system is formulated at the finest scale with multiple sensors at different scales observing a common target independently with different sampling rates. The noise between different sensors is relevant, moreover, is also correlated with the system noise. The authors derive the local state estimation algorithms under the circumstance of total reliable measurements and stochastic unreliable measurements occur occasions, and the optimal distributed Kalman filter fusion algorithm, respectively. The authors provide a simulation example to illustrate the effectiveness and feasibility of the proposed algorithm.

Karst Collapse Monitoring and Early Warning Evaluation Method Based on Multisensor Internet of Things

“…If the sampling data of a sensor are supported by all other sensors, the sampling data are valid. If the sampled data of a certain sensor are only supported by a very small number of sensors, it is considered that the sampling data of the sensor is invalid and should be eliminated in the fusion stage to improve the fusion efficiency [ 24 ] because in the actual environment, the choice of threshold is too subjective and absolute. It often leads to misjudgment of fusion results.…”

Parameter identification for Hammerstein nonlinear system with polynomial and state space model

“…42,43 State space systems have been successfully applied to parameter identification for a long history with many theoretical formulations. [44][45][46] Gu et al investigated a moving horizon estimation approach for multirate sampled-data system with unknown time-delay sequence, which realized simultaneously discrete timedelay sequence estimation and continuous state estimation. 44 In Ansari and Bernstein, 45 based on the generalized inverse of block-Toeplitz matrix, deadbeat unknown-input state estimation and simultaneous input reconstruction and state estimation for multiinput multi-output system are researched.…”

“…In view of a kind of linear dynamic multirate sensors systems with correlated noise and stochastic unreliable measurements, Yan et al studied the optimal distributed Kalman filter fusion algorithm. 46 The above existing works assume that the system studied is linear, on the contrary, most of the practical systems are subject to nonlinear characteristics.…”

Bias compensation‐based parameter and state estimation for a class of time‐delay non‐linear state‐space models

“…Identification of the state‐space systems has received considerable attention in the past few decades [24–26]. There are various identification methods, representatively such as the subspace identification algorithm, the recursive least‐squares algorithm, and the Kalman filter algorithm [27].…”