Multiple Object Trajectography Using Particle Swarm Optimization Combined to Hungarian Method

TL;DR

This paper presents a novel approach combining particle swarm optimization with the Hungarian method to solve the complex problem of multiple object trajectography from photographic observations, demonstrated in space orbitography.

Contribution

It introduces a hybrid optimization technique that integrates assignment and trajectory estimation for multiple objects, improving accuracy in trajectography tasks.

Findings

Effective in space orbitography applications

Reduces deviation between estimated and observed trajectories

Demonstrates improved accuracy over traditional methods

Abstract

The problem of simultaneous trajectography of several dynamical objects is formulated as an optimization problem. The available observations consist in a series of photographs showing undiscriminated objects. The goal is to find the object initial states so that the resulting trajectories match as well as possible the set of observations. An assignment problem is solved at each observation date by the Hungarian method, yielding a deviation cost between the simulated trajectories and the measurements. A fitness function summing the deviation costs is minimized by a particle swarm algorithm. The method is illustrated on a space orbitography application.