TITLE:
Enhanced Bilinear Approach for Sensor Network Self-Localization Using Noisy TOF Measurements
AUTHORS:
Xue Gao, Le Yang, Li Peng
KEYWORDS:
Self-Localization, Time of Flight (TOF), Global Coordinate System, Least Squares Estimation
JOURNAL NAME:
Journal of Computer and Communications,
Vol.2 No.7,
May
21,
2014
ABSTRACT: This paper develops a new algorithm for
sensor network self-localization, which is an enhanced version of the existing
Crocco’s method in [11]. The algorithm explores the noisy time of flight (TOF)
measurements that quantify the distances between sensor nodes to be localized
and sources also at unknown positions. The newly proposed technique first
obtains rough estimates of the sensor node and source positions, and then it
refines the estimates via a least squares estimator (LSE). The LSE takes into
account the geometrical constraints introduced by the desired global coordinate
system to improve performance. Simulations show that the new technique offers
superior localization accuracy over the original Crocco’s algorithm under small
measurement noise condition.