Journal of Northeastern University Natural Science ›› 2018, Vol. 39 ›› Issue (7): 913-917.DOI: 10.12068/j.issn.1005-3026.2018.07.001

• Information & Control •     Next Articles

Particle Swarm Optimization Localization Algorithm Based on Sample Mean and Median

HUANG Yue-yang1,2, JING Yuan-wei1, ZHANG Si-ying1, SHI Yuan-bo1   

  1. 1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China; 2. School of Information and Control Engineering, Liaoning Shihua University, Fushun 113001, China.
  • Received:2017-03-21 Revised:2017-03-21 Online:2018-07-15 Published:2018-07-11
  • Contact: HUANG Yue-yang
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Abstract: The non-line-of-sight(NLOS)state was determined by the method of hypothesis testing for indoor line-of-sight/non-line-of-sight (LOS/NLOS) hybrid environment. And particle swarm optimization algorithm with shrinkage factor was used to locate. When the sampling value was abnormal, the performance of sample median was better than sample mean. Minimum square error cost functions of the sample mean and sample median were established in LOS and NLOS state. In order to enhance the global and local search ability of the algorithm, the shrinkage factor was introduced on the basis of particle swarm optimization algorithm. Simulation results show that the improved particle swarm optimization algorithm based on sample mean and median (IPSOSMM)has higher localization accuracy than that of the improved particle swarm optimization algorithm based on sample mean (IPSOSM)and the general particle swarm optimization algorithm when the NLOS block is serious.

Key words: localization, non-line of sight, sample mean, sample median, particle swarm optimization

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