Weighted Total Least Squares Based On-Line Calibration Method for RSS Based Localization

Jung-Hee Kim1, Doik Kim2

  • 1Korea Institute of Science and Technology
  • 2KIST

Details

09:00 - 09:03 | Tue 2 Oct | Room 1.L2 | TuATS3.1

Session: Localization and Mapping I

Abstract

In the received signal strength (RSS) based localization, a model-based calibration approach has been usually done by relating RSS-to-distance among anchor nodes. In this paper, an improved calibration method is proposed. For that purpose, RSS and estimated distance between any pairs of anchor/unknown nodes is considered under the linear regression model. Unfortunately in this model, partial input elements are erroneous due to the inaccurate localization of unknown nodes. To obtain its solution under consideration of such an error, the weighted total least squares (WTLS) techniques are employed here. With the help of the WTLS techniques, several errors involved in the model can be effectively compensated. To show the efficiency of the proposed calibration, it is combined with several localization algorithms and its performance is verified by various simulations. The results show that the proposed calibration can give a very similar localization performance to that of each localization algorithm when true model parameters are known.