Set-membership vs. stochastic approaches for target localization with UAVs

Abstract

Searching and tracking mobile targets rely most often on modeling the uncertainty and various perturbations by stochastic processes. The detection and location of the targets are performed with Bayesian estimation, which reliability and resulting performance are deeply linked to the adequacy of the stochastic models. An alternative approach limits the representation of these perturbations by defining the bounds within which they can vary. Set-membership estimation techniques have been developed to handle this representation. This paper compares the performance of set-membership and stochastic Bayesian estimation techniques for target search and tracking for scenarios integrating false alarms. For this purpose, estimation schemes are presented for each approach. The ability of estimators to find real targets and not to be deceived by false targets or imperfect sensors are compared in simulations.