Extremal optimization for sensor report pre-processing

TL;DR

This paper applies extremal optimization to efficiently pre-process large sensor reports for multi-target tracking, reducing computational complexity while maintaining effective association and clustering.

Contribution

It introduces an extremal optimization approach for sensor report pre-processing, leveraging phase-transition insights to approximate conflict calculations.

Findings

Effective pre-processing of large sensor reports demonstrated

Reduced computational complexity in target detection tasks

Utilized phase-transition phenomena to guide approximation

Abstract

We describe the recently introduced extremal optimization algorithm and apply it to target detection and association problems arising in pre-processing for multi-target tracking. Here we consider the problem of pre-processing for multiple target tracking when the number of sensor reports received is very large and arrives in large bursts. In this case, it is sometimes necessary to pre-process reports before sending them to tracking modules in the fusion system. The pre-processing step associates reports to known tracks (or initializes new tracks for reports on objects that have not been seen before). It could also be used as a pre-process step before clustering, e.g., in order to test how many clusters to use. The pre-processing is done by solving an approximate version of the original problem. In this approximation, not all pair-wise conflicts are calculated. The approximation relies on knowing how many such pair-wise conflicts that are necessary to compute. To determine this, results on phase-transitions occurring when coloring (or clustering) large random instances of a particular graph ensemble are used.