FM-based indoor localization via automatic fingerprint DB construction and matching

  • Authors:

  • Sungro Yoon;Kyunghan Lee;Injong Rhee

  • Affiliations:

  • North Carolina State University, Raleigh, NC, USA;Ulsan National Institute of Science and Technology, Ulsan, South Korea;North Carolina State University, Raleigh, NC, USA

  • Venue:
  • Proceeding of the 11th annual international conference on Mobile systems, applications, and services
  • Year:
  • 2013

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Abstract

We present ACMI, an FM-based indoor localization that does not require proactive site profiling. ACMI constructs the fingerprint database based on the pure estimation of indoor RSS distribution, where the signals transmitted from commercial FM radio stations are used. For this, ACMI makes use of our signal model harnessing public transmission information of FM stations in a combination with a floorplan of a building. Using the fingerprint database as the knowledge base, ACMI actively performs multi-level online signal matching to infer the current location of a mobile user. ACMI achieves good indoor localization accuracy even without site profiling efforts. We evaluate ACMI with extensive indoor experiments in 7 different locations with over 1,100 indoor spots. The results show that ACMI achieves up to 89% room identification and accuracy of 6m localization error on average using 8 FM broadcast signals.