Optimal measurement budget allocation for particle filtering

TL;DR

This paper addresses the challenge of optimally allocating limited measurement resources in particle filtering for target tracking by formulating it as a combinatorial optimization problem and proposing an effective hybrid solution.

Contribution

It introduces a novel approach combining genetic, Monte Carlo, and particle filter algorithms to optimize measurement timing under resource constraints.

Findings

Genetic algorithm outperforms random trial optimization.

Non-regular measurements improve filtering performance.

Proposed method achieves 87.5% better performance with an average of 27.7% improvement.

Abstract

Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter. Firstly, an example demonstrates that the genetic algorithm outperforms a random trial optimization. Then, the interest of non-regular measurements versus measurements performed at regular time intervals is illustrated and the efficiency of our proposed solution is quantified: better filtering performances are obtained in 87.5% of the cases and on average, the relative improvement is 27.7%.