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Open Access

LoSI: Large Scale Location Inference Through FM Signal Integration and Estimation

Department of Computer Science, University of Alabama in Huntsville, Huntsville, AL 35806, USA.
CompGeom Inc., Tallahassee, FL 32311, USA.
Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
Air Force Research Laboratory, Rome, NY 13441, USA.
Air Force Research Laboratory, Shalimar, FL 32579, USA.
CompGeom Inc., Tallahassee, FL 32579, USA.
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In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System (GPS) denied/spoofed environments. This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching (TERCOM). Our Location inference through Frequency Modulation (FM) Signal Integration and estimation (LoSI) system is based on broadcast FM radio signals and uses Received Signal Strength Indicator (RSSI) obtained using a Software Defined Radio (SDR). The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States. We show that with the hardware for data acquisition, a single point resolution of around 3 miles and associated algorithms, we are capable of positioning with errors as low as a single pixel (more precisely around 0.12 mile). The algorithm uses a large-scale model estimation phase that computes the expected FM spectrum in small rectangular cells (realized using geohashes) across the Contiguous United States (CONUS). We define and use Dominant Channel Descriptor (DCD) features, which can be used for positioning using time varying models. Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation. The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates (IC). Finally, it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation. We report results on 1500 points across Florida using data and model estimates from 2015 and 2017. We also provide a Bayesian decision theoretic justification for the nearest neighbor search.


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Big Data Mining and Analytics
Pages 319-348
Cite this article:
Mukherjee T, Kumar P, Pati D, et al. LoSI: Large Scale Location Inference Through FM Signal Integration and Estimation. Big Data Mining and Analytics, 2019, 2(4): 319-348.








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Received: 05 May 2019
Revised: 03 June 2019
Accepted: 04 June 2019
Published: 05 August 2019
© The author(s) 2019

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