Track-To-Track Association for Fusion of Dimension-Reduced Estimates

TL;DR

This paper introduces a new approach for network-centric multitarget tracking that reduces estimate dimensions based on association performance, balancing communication constraints with tracking accuracy.

Contribution

It proposes a novel problem formalization and optimization strategy to preserve association quality during dimension reduction of track estimates.

Findings

Theoretical analysis of the new formalization

Optimization strategy to maintain association quality

Numerical demonstration in multitarget scenario

Abstract

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.