An Efficient Construction of Confidence Regions via Swarm Intelligence and Its Application in Target Localization | Zendy

Frank Po-Chen Lin | Zendy; Frederick Kin Hing Phoa | Zendy

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An Efficient Construction of Confidence Regions via Swarm Intelligence and Its Application in Target Localization

Author(s) -

Frank Po-Chen Lin,

Frederick Kin Hing Phoa

Publication year - 2017

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2017.2785789

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

It is essential to enhance the speed and accuracy of the localization process to gain the robustness and instantaneous properties and to adapt from the practical environment of a confidence band. In this paper, we proposed a new received signal strength indicator-based method to construct a realtime confidence band, which was composed by multiple confidence region sets in a multivariate normal distribution, associated to a target's trajectory for location-based services. Based on the concept of weighted positioning circular algorithm, we designed a new objective function to take into consideration the signal disruptions of the surrounding environments. The characteristics of the state of motion for the moving target were then inferred from the status of each confidence region. In order to speed up the localization process to obtain the real-time estimate of the confidence band via our objective function, we proposed in this paper a swarm intelligence-based localization optimization algorithm, which was modified from the standard framework of a novel swarm intelligence-based evolutionary algorithm.

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