Comparison of performances of GM and SMC implementations of CB-MeMBer filter for sensor control

In this work, sequential Monte Carlo and Gaussian mixture implementations of cardinality balanced multi-Bernoulli filter, developed under random finite set theory framework, are compared for sensor control application. In the simulations, two different types of reward/penalty functions are utilized. They are based on reduction of uncertainty and information gain. These functions are calculated using partially observable Markov decision processes framework. The sensors move according to the outputs of these functions. The formulations for sequential Monte Carlo methods can already be found in the literature. However, there is not much work done on Gaussian mixtures. Gaussian mixtures based formulations are presented in this work. These two different implementations are compared for different sensor types, reward/penalty functions. In order to give an idea on a possible implementation on a real application, run times of the algorithms are also presented