Multisensor decentralized nonlinear fusion using adaptive cubature information filter

Binglei Guan,Xianfeng Tang

doi:10.1371/journal.pone.0241517

Binglei Guan, Xianfeng Tang

Open Access

https://doi.org/10.1371/journal.pone.0241517

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Abstract

In nonlinear multisensor system, abrupt state changes and unknown variance of measurement noise are very common, which challenges the majority of the previously developed models for precisely known multisensor fusion techniques. In terms of this issue, an adaptive cubature information filter (CIF) is proposed by embedding strong tracking filter (STF) and variational Bayesian (VB) method, and it is extended to multi-sensor fusion under the decentralized fusion framework with feedback. Specifically, the new algorithms use an equivalent description of STF, which avoid the problem of solving Jacobian matrix during determining strong trace fading factor and solve the interdependent problem of combination of STF and VB. Meanwhile, A simple and efficient method for evaluating global fading factor is developed by introducing a parameter variable named fading vector. The analysis shows that compared with the traditional information filter, this filter can effectively reduce the data transmission from the local sensor to the fusion center and decrease the computational burden of the fusion center. Therefore, it can quickly return to the normal error range and has higher estimation accuracy in response to abrupt state changes. Finally, the performance of the developed algorithms is evaluated through a target tracking problem.

Highlights

For function and purpose of data fusion, multisensor data fusion is defined as the combination of data from a variety of sources that provides inferences of a quality far superior to any single sensor or single information source [1]
We introduce strong tracking filter technology and variational Bayesian technology to improve cubature information filter (CIF), and propose an adaptive CIF (ACIF-STF-VB)
The results show that ACIF-STF-VB has better accuracy than VB-ACIF

Summary

Introduction

For function and purpose of data fusion, multisensor data fusion is defined as the combination of data from a variety of sources (including sensors, or data from the network reporting) that provides inferences of a quality far superior to any single sensor or single information source [1]. In many nonlinear filtering or fusion problems considered in practice, the models of system are usually uncertain These uncertainties include the sudden change of state and unknown variance of measurement noise. Multisensor decentralized nonlinear fusion using adaptive cubature information filter the state vector and the covariance matrix can be obtained by For the CIF, a modified state prediction error covariance with the fading factor λ(k) is given in [16,17]. In order to guarantee the normal execution of the algorithm, we need to compute pseudo measurement matrix H(k) and innovation γ(k) Those parameters can be evaluated as follows: 1.

Compute the covariance of measurement noise

DT 0 0

Findings

Conclusions