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Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Multisensor decentralized nonlinear fusion using adaptive cubature information filter.

Binglei Guan^1,2, Xianfeng Tang³

¹Logistics Engineering College, Shanghai Maritime University, Shanghai, China.

|November 5, 2020

Summary

This study introduces an adaptive cubature information filter (CIF) for nonlinear multisensor systems. The new filter enhances state estimation accuracy and reduces computational load, especially during abrupt changes.

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Area of Science:

Engineering
Information Science

Background:

Nonlinear multisensor systems frequently experience abrupt state changes and unknown measurement noise variance.
Existing multisensor fusion techniques struggle with these challenges due to precise parameter requirements.

Purpose of the Study:

To propose an adaptive cubature information filter (CIF) for decentralized multisensor fusion.
To enhance robustness against abrupt state changes and unknown noise variances.

Main Methods:

Integration of the Strong Tracking Filter (STF) and Variational Bayesian (VB) method into the CIF.
Development of a decentralized fusion framework with feedback.
Introduction of a fading vector for efficient global fading factor evaluation, avoiding Jacobian matrix computation.

Main Results:

The proposed adaptive CIF effectively reduces data transmission and computational burden.
The filter demonstrates improved accuracy and faster recovery from abrupt state changes compared to traditional methods.
Performance validated through a target tracking problem.

Conclusions:

The adaptive cubature information filter offers a robust and efficient solution for nonlinear multisensor fusion.
The method successfully addresses challenges posed by abrupt state changes and unknown noise characteristics.
The decentralized framework with feedback enhances practical applicability.