Unscented particle filtering algorithm for optical-fiber sensing intrusion localization based on particle swarm optimization

To improve the convergence and precision of intrusion localization in optical-fiber sensing perimeter protection applications, we present an algorithm based on an unscented particle filter (UPF). The algorithm employs particle swarm optimization (PSO) to mitigate the sample degeneracy and impoverishment problem of the particle filter. By comparing the present fitness value of particles with the optimum fitness value of the objective function, PSO moves particles with insignificant UPF weights towards the higher likelihood region and determines the optimal positions for particles with larger weights. The particles with larger weights results in a new sample set with a more balanced distribution between the priors and the likelihood. Simulations demonstrate that the algorithm speeds up convergence and improves the precision of intrusion localization.