Abstract
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 largescale 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.
Highlights
Localization using ambient wireless signals has generated considerable interest in recent times
Received signal strength deviation (b) across the curves in Fig. 8 is only 0.617 dBm. This is important for the following reasons: (1) The plot shows that if we look at the data after the Dominant Channel Descriptor (DCD) extraction, the difference between the standard deviations across the Gaussians is very small
Though we provide results for the Kendall-Taubased localization, first use in Ref. [3] for large scale positioning with Frequency Modulation (FM) within the confines of a city, we want to compare the results of our algorithm with those obtained from other large scale positioning algorithms that are not based on FM
Summary
Localization using ambient wireless signals (both indoor as well as outdoor) has generated considerable interest in recent times. Indoor localization is important for ubiquitous computing[7,8] and as Global Positioning System (GPS) is severely degraded in such environments[9], other modes of localization become necessary. Absolute positioning can be done using two approaches. The first approach relies on communications with the GPS whereas the second achieves its objective without any such communication. Traditional GPS-based localization[14] uses GPS receivers to communicate with several GPS satellites. The received data is used to compute the distance of the object from at least four known GPS satellites using the idea of TOA[36]. Current GPS systems, without modifications, suffer from several limitations, namely, lack of precision[44], jamming[45], and disruption and spoofing[46]
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