Enhanced Bilinear Approach for Sensor Network Self-Localization Using Noisy TOF Measurements

Xue Gao, Le Yang, Li Peng

School of Internet of Things (IoT) Engineering, Jiangnan University, Wuxi, China.

DOI: 10.4236/jcc.2014.27004                

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.

Keywords

Self-Localization, Time of Flight (TOF), Global Coordinate System, Least Squares Estimation

Conflicts of Interest

The authors declare no conflicts of interest.

References

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