  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%.
