A Genetic Optimization Resampling Based Particle Filtering Algorithm for Indoor Target Tracking

In indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly non-linear problems. Resampling is generally required to address inherent degeneracy problem filter. However, traditional resampling methods cause impoverishment. This degrades positioning accuracy and robustness sometimes may even result divergence failure. order mitigate impoverishment improve accuracy, this paper proposes an improved genetic optimization method. method optimizes distribution resampled particles by five operators, i.e., selection, roughening, classification, crossover, mutation. The proposed then integrated into framework form a (GORPF) algorithm. GORPF tested one-dimensional simulation three-dimensional experiment. Both test results show that aid method, better against achieves than several existing algorithms. Moreover, owns affordable computation load for real-time applications.