A Weighted Consistency State Filtering Method for Distributed Information in Sensor Networks

Abstract

The invention discloses a sensor network distributed information weighted consistency state filtering method, which belongs to the sensor information fusion technology and relates to the distributed nonlinear state estimation problem of the sensor network. The filtering method first uses the square root volume rule to obtain the prediction information vector and square root information matrix of each node for the target state; then each node updates the local state information through the weighted local prediction information and observation information vector, and through the weighted local prediction information and observation information matrix, The local square root information matrix is ​​updated by combining the triangular decomposition of the matrix; finally, the weighted consistency of the information between neighboring nodes is used to achieve the consistency of the target information of the whole network, and the estimation of the target state is completed. The method improves the consistent convergence speed of state estimation in the network, enhances the numerical stability of the algorithm, and can improve the accuracy of target state estimation under the condition of limited energy.

Description

technical field [0001] The invention belongs to the sensor information fusion technology, relates to the sensor network distributed nonlinear state estimation problem, and provides a square root volumetric filtering method based on consistent information weighting. Background technique [0002] In the research field of multi-sensor target state estimation, traditional methods mostly adopt a centralized structure, each local node sends its own detection information to the fusion center, and the fusion center centrally processes all the received information before distributing it to Local nodes to realize the sharing of estimation results. During the whole processing process, the fusion center needs to receive and process a large amount of observation information. Although the estimation accuracy of the target state is high, it will cause excessive communication and calculation consumption of the network, and the real-time estimation cannot be guaranteed. In addition, interme...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the limited detection and communication capabilities of nodes, it is difficult to ensure that each node and its neighbors can observe the target during the tracking process, that is, there are naive nodes (NaiveNodes) in the network. The state estimation accuracy of the target is limited

At the same time, the state model of the target and the observation model of the sensor are often nonlinear. The traditional KCF method combines the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF), and the volumetric Kalman filter. Although filtering (Cubature Kalman Filter, CKF) and other means can achieve effective estimation of the target state, but because the information interaction weight of each neighbor node is the same, the convergence speed of the consistent state is slow

At the same time, due to the limited energy of each node, the number of information iterations of each node in the network is limited, and it is impossible to guarantee that the estimated states of all nodes in the network are consistent at each time.

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