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

作     者：Tathagata Mukherjee Piyush Kumar Debdeep Pati Erik Blasch Eduardo Pasiliao Liqin Xu 

作者机构：Department of Computer ScienceUniversity of Alabama in HuntsvilleHuntsvilleAL 35806USA CompGeom Inc.TallahasseeFL 32311USA Department of StatisticsTexas A&M UniversityCollege StationTX 77843USA Air Force Research LaboratoryRomeNY 13441USA Air Force Research LaboratoryShalimarFL 32579USA CompGeom Inc.TallahasseeFL 32579USA 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2019年第2卷第4期

页      面：319-348页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 081802[工学-地球探测与信息技术] 0839[工学-网络空间安全] 08[工学] 081105[工学-导航、制导与控制] 0818[工学-地质资源与地质工程] 0804[工学-仪器科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Global Positioning System(GPS)-free positioning Frequency Modulation(FM) radio signals of opportunity nearest neighbor search 

摘      要：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.