Toward Emergency Indoor Localization: Maximum Correntropy Criterion Based Direction Estimation Algorithm for Mobile TOA Rotation Anchor | Zendy

Liyuan Xu | Zendy; Jie He | Zendy; Peng Wang | Zendy; Kaveh Pahlavan | Zendy; Huansheng Ning | Zendy; Qin Wang | Zendy

Open Access

Toward Emergency Indoor Localization: Maximum Correntropy Criterion Based Direction Estimation Algorithm for Mobile TOA Rotation Anchor

Author(s) -

Liyuan Xu,

Jie He,

Peng Wang,

Kaveh Pahlavan,

Huansheng Ning,

Qin Wang

Publication year - 2018

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.2018.2850967

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

To find the relative positional relationship accurately and efficiently without pre-installed infrastructure in emergent scenarios like firefighting, we proposed a rotating-anchor based indoor geolocation system using time of arrival (TOA) technique in this paper. In this system, the positioning problem is regarded as a template-matching problem to estimate direction of the target. Based on preliminary results, further measurements that the tester held the locator and turned around continuously without any stop. The TOA ranging error for continuous rotation measurement was analyzed and proved to follow non-Guassian with impulsive characteristic in this paper. According to such error characteristics, a direction estimation algorithm of maximum correntropy criterion-based matching algorithm was proposed in this paper. The performance of algorithm was validated by comparing with the least mean square and least mean p-norm with empirical measurements.

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