Distributed sensor network consistency filtering method with unknown colored noise

Abstract

The invention provides a distributed sensor network consistency filtering method with unknown colored noise, which comprises the following steps of: learning state noise and measurement noise parameters of each node in a distributed sensor network according to a group of given training data; meanwhile, performing state extension, extending learned colored noise parameters to a state vector, and finally, state estimation is completed by using a consistency Kalman filtering algorithm, so that estimated values of all sensor nodes are converged to be globally consistent. To improve computational efficiency, only local measurement noise and state noise are integrated into an extended state for each node. Simulation results show that the unknown colored noise in the distributed sensor network can be effectively estimated, and the accuracy of distributed estimation is improved. The method can be applied to the fields of distributed sensor network radars, infrared target tracking, mobile robot positioning and the like.

Description

technical field [0001] The invention relates to a method for consistent filtering of a distributed sensor network with unknown colored noise. Background technique [0002] In the sensor network, due to the strong scalability and high fault tolerance of the distributed state estimation algorithm, its application is more and more extensive. The algorithm has low requirements on network connectivity and system observability; maintaining global observability of the network does not necessarily mean that every sensor is observable. Furthermore, the fusion center does not need to collect raw measurements from all nodes in the sensor network. The distributed strategy therefore provides a more optimal choice, is more robust, requires less communication and allows parallel processing. On the contrary, the centralized fusion algorithm will generate a large amount of communication and computing burden at the central node. [0003] There are three different methods for distributed st...

AI Technical Summary

Problems solved by technology

[0004] The traditional consensus filtering algorithm requires the system to have Gaussian noise. However, for the system in the state of colored noise, the classic consensus algorithm cannot perform effective state estimation.

In view of the presence of colored noise in the measurement equations in distributed sensor networks, the method of state extension and measurement difference can be applied to the consistency filtering algorithm, but the situation of colored noise with unknown parameters is not considered

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