Distribution type non-linear filtering method

Abstract

The invention relates to a distribution type non-linear filtering method which belongs to the technical field of signal processing. Each sensor node in a network carries out weighing statistical linearization on a target state equation and a measuring equation to obtain a weighing statistical linearized system matrix, a measuring matrix, and linearized system noise and measuring noise; the sensor node carries out communication with the adjacent node to exchange local information contribution and obtains the overall information contribution according to a dynamic cooperation filter; the sensor node predicts the current state of the target according to the weighing statistical linearized target state equation and calculates the corresponding variance matrix; then, the target state is refreshed according to the overall information contribution obtained by the cooperation filter in the step 2, and thus, the current state evaluating valve of the target is obtained. The invention reduces the communication complexity of the filtering processing. When the traffic volume for dispersing an unscented Kalman filter is O (2450), the traffic volume required by the method is reduced to O (230).

Description

Technical field [0001] The invention relates to a method in the technical field of signal processing, in particular to a scalable distributed nonlinear filtering method. Background technique [0002] With the development of computer networks, wireless communications, and micro-small systems, wireless sensor networks that integrate the above three technologies have emerged. The wireless sensor network brings a new information acquisition and processing mode, which will profoundly affect the future development of information technology. Target tracking is the most representative and challenging application of wireless sensor networks. Target tracking can be divided into two ways: centralized and distributed. Centralized tracking can achieve high-precision data processing, but due to the large amount of data required, the demanding requirements for the fusion center, and the long processing time, packet loss and delay are prone to occur, which reduces the tracking accuracy. Distr...

AI Technical Summary

Problems solved by technology

[0003] After searching the prior art, it is found that traditional decentralized (decentralized) filtering and fusion methods, such as decentralized Kalman filter and decentralized unscented Kalman filter (T.Vercauteren, and X.Wang, "Decentralized sigma-point information filters for target tracking in collaborative sensor networks,"IEEE T.SignalProcessing, vol.53, no.8, pp.2997-3009, Aug.2005), are global to global, that is, each node needs to communicate with the network All other nodes or fusion centers communicate, so the communication complexity required for the above two filtering or fusion methods is O(N*N), where N is the number of sensor nodes or agents in the network)

Obviously, due to the high communication complexity, the above methods cannot be scaled, especially for large-scale sensor networks.

Images