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