A fuzzy-logic-based approach for soft data constrained multiple-model PHD filter

Tracking multiple targets with non-linear dynamics is a challenging problem. One of the popular solutions, Sequential Monte Carlo-Probability Hypothesis Density (SMC-PHD) filter, deploys a Random Set (RS) theoretic formulation along with the Sequential Monte Carlo approximation, which is a variant of Bayes filtering. The performance of Bayesian filtering-based methods can be enhanced by using extra information incorporated as specific constraints into the filtering process. Following the same principle, this paper proposes a constrained variant of the SMC-PHD filter, in which the inherently vague human-generated data are transformed into a set of constraints using a fuzzy logic approach. These constraints are enforced to the filtering process by applying coefficients to the particles´ weights. The Soft Data (SD) reports on target agility level; wherein, the agility refers to the case in which the observed dynamics of the targets deviates from its given probabilistic characterization. Consequently, the proposed constrained filtering approach enables dealing with multitarget tracking scenarios in presence of target agility, as demonstrated by the experimental results presented in this paper.