Unscented Particle Filtering Algorithm for Optical-Fiber Sensing Intrusion Localization Based on Particle Swarm Optimization
Abstract: 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 theoptimum
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.
Author: Hua Zhang, Xiaoping
Jiang, Chenghua Li
Journal Code: jptkomputergg150064
