Bias Estimation for Decentralized Sensor Fusion -- Multi-Agent Based Bias Estimation Method
This work provides a solution for bias estimation in decentralized sensor fusion systems, which is relevant for multi-sensor tracking applications where centralized processing is infeasible.
The paper proposes a multi-agent based bias estimation method for decentralized sensor fusion, enabling bias estimation and compensation without requiring centralized data collection. The method addresses the limitation of existing approaches that cannot fuse track data from individual sensor nodes.
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.