Bias Estimation for Decentralized Sensor Fusion -- Multi-Agent Based Bias Estimation Method

TL;DR

This paper introduces a decentralized bias estimation method for multi-sensor fusion that allows individual sensor nodes to estimate and compensate biases without centralized data collection, improving tracking accuracy.

Contribution

It presents a novel multi-agent based bias estimation approach enabling decentralized sensor fusion with bias correction at each sensor node.

Findings

Supports decentralized bias estimation and compensation

Enhances accuracy of multi-sensor tracking

Operates without centralized data collection

Abstract

In multi-sensor data fusion (or sensor fusion), sensor biases (or offsets) often affect the accuracy of the correlation and integration results of the tracking targets. Therefore, to estimate and compensate the bias, several methods are proposed. However, most methods involve bias estimation and sensor fusion simultaneously by using Kalman filter after collecting the plot data together. Hence, these methods cannot support to fuse the track data prepared by tracking filter at each sensor node. This report proposes the new bias estimation method based on multi-agent model, in order to estimate and compensate the bias for decentralized sensor fusion.