Distributed information weighted coherent state filtering method for sensor network

Abstract

The invention discloses a distributed information weighted coherent state filtering method for a sensor network, belonging to sensor information fusion technology, which relates to a distributed nonlinear state estimation problem of the sensor network. Firstly, a square root volume rule is used to obtain the prediction information vector and the square root information matrix of each node to the target state. Then, each node updates local state information by weighted local prediction information and observation information vector, updates local square root information matrix by weighted localprediction information and observation information matrix, and combines matrix triangular decomposition. Finally, the information consistency of the whole network is achieved by using the weighted consistency of the neighboring nodes, and the estimation of the target state is completed. This method improves the convergence rate of the state estimation in the network, enhances the numerical stability of the algorithm, and improves the accuracy of the state estimation of the target 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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