Distributed Extended Object Tracking Information Filter Over Sensor Networks

TL;DR

This paper develops a distributed extended object tracking system over sensor networks using a novel information filter approach that maintains consensus on object extent and kinematics, with proven stability and superior performance.

Contribution

It introduces a new distributed information filter based on the multiplicative error model for extended object tracking, preserving cross-correlation and ensuring exponential error bounds.

Findings

The proposed filter achieves better accuracy than existing methods.

The filter maintains stability with exponentially bounded errors.

Numerical experiments validate improved performance over state-of-the-art filters.

Abstract

This work aims to design a distributed extended object tracking (EOT) system over a realistic network, where both the extent and kinematics are required to retain consensus within the entire network. To this end, we resort to the multiplicative error model (MEM) that allows the extent parameters of perpendicular axis-symmetric objects to have individual uncertainty. To incorporate the MEM into the information filter (IF) style, we use the moment-matching technique to derive two pair linear models with only additive noise. The separation is merely in a fashion, and the cross-correlation between states is preserved as parameters in each other's model. As a result, the closed-form expressions are transferred into an alternating iteration of two linear IFs. With the two models, a centralized IF is proposed wherein the measurements are converted into a summation of innovation parts. Later, under a sensor network with the communication nodes and sensor nodes, we present two distributed IFs through the consensus on information and consensus on measurement schemes, respectively. Moreover, we prove the estimation errors of the proposed filter are exponentially bounded in the mean square. The benefits are testified by numerical experiments in comparison to state-of-the-art filters in literature.