Articles | Volume 6, issue 1
J. Sens. Sens. Syst., 6, 247–251, 2017
https://doi.org/10.5194/jsss-6-247-2017
J. Sens. Sens. Syst., 6, 247–251, 2017
https://doi.org/10.5194/jsss-6-247-2017

Regular research article 20 Jun 2017

Regular research article | 20 Jun 2017

Scanning method for indoor localization using the RSSI approach

Ahmad Warda1, Bojana Petković2, and Hannes Toepfer1 Ahmad Warda et al.
  • 1Technische Universität Ilmenau, Institute for Information Technology, Ilmenau, Germany
  • 2Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Ilmenau, Germany

Abstract. This paper presents a scanning method for indoor mobile robot localization using the received signal strength indicator (RSSI) approach. The method eliminates the main drawback of the conventional fingerprint, whose database construction is time-consuming and which needs to be rebuilt every time a change in indoor environment occurs. It directly compares the column vectors of a kernel matrix and signal strength vector using the Euclidean distance as a metric. The highest resolution available in localization using a fingerprint is restricted by a resolution of a set of measurements performed prior to localization. In contrast, resolution using the scanning method can be easily changed using a denser grid of potential sources. Although slightly slower than the trilateration method, the scanning method outperforms it in terms of accuracy, and yields a reconstruction error of only 0. 08 m averaged over 1600 considered source points in a room with dimensions 9.7 m × 4.7 m × 3 m. Its localization time of 0. 39 s makes this method suitable for real-time localization and tracking.

Download
Short summary
We studied the problem of wireless indoor mobile robot localization and tracking using noise-free data and data with additive white Gaussian noise at three receiver positions. We proposed a new scanning method to overcome the drawbacks of fingerprint, which includes time-consuming construction of a database and its need for rebuilding every time a significant change in the environment occurs.