Algorithms for the multi-sensor assignment problem in the δ-generalized labeled multi-Bernoulli filter

Abstract

Previous adaptations of the δ-generalized labeled multi-Bernoulli (δ-GLMB) filter to the multi-sensor case involve the sequential application of the update step for each sensor or Gibbs sampling for multi-sensor data association. The practical usage of the sequential δ-GLMB filter is limited due to the number of hypotheses growing with each additional sensor. Similarly, the Gibbs method requires a large number of samples for each hypothesis. In this paper, in the aim of finding the optimal or near-optimal multi-sensor assignments, we propose two novel methods, the combination and the cross entropy methods. Numerical simulations are conducted to evaluate the proposed multi-assignment methods together with the standard sequential processing method and a stochastic optimization algorithm based on Gibbs sampling. The combination method is able to significantly reduce running time with respect to the sequential method while yielding competitive performance across a wide range of test scenarios.