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

Big Data Mining and Analytics 2019年第4期2卷 319-348页

作者： Tathagata Mukherjee Piyush Kumar debdeep pati Erik Blasch Eduardo Pasiliao Liqin Xu Department of Computer Science University of Alabama in HuntsvilleHuntsvilleAL 35806USA CompGeom Inc. TallahasseeFL 32311USA Department of Statistics Texas A&M UniversityCollege StationTX 77843USA Air Force Research Laboratory RomeNY 13441USA Air Force Research Laboratory ShalimarFL 32579USA CompGeom Inc. TallahasseeFL 32579USA

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.

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

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Bayesian optimization with adaptive surrogate models for automated experimental design

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npj Computational Materials 2021年第1期7卷 1800-1811页

作者： Bowen Lei Tanner Quinn Kirk Anirban Bhattacharya debdeep pati Xiaoning Qian Raymundo Arroyave Bani K.Mallick Department of Statistics Texas A&M UniversityCollege StationTXUSA Department of Mechanical Engineering Texas A&M UniversityCollege StationTXUSA Department of Electrical&Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Computer Science&Engineering Texas A&M UniversityCollege StationTXUSA Department of Materials Science&Engineering Texas A&M UniversityCollege StationTXUSA

Bayesian optimization(BO)is an indispensable tool to optimize objective functions that either do not have known functional forms or are expensive to ***,optimal experimental design is always conducted within the workflow of BO leading to more efficient exploration of the design space compared to traditional *** can have a significant impact on modern scientific discovery,in particular autonomous materials discovery,which can be viewed as an optimization problem aimed at looking for the maximum(or minimum)point for the desired materials *** performance of BO-based experimental design depends not only on the adopted acquisition function but also on the surrogate models that help to approximate underlying objective *** this paper,we propose a fully autonomous experimental design framework that uses more adaptive and flexible Bayesian surrogate models in a BO procedure,namely Bayesian multivariate adaptive regression splines and Bayesian additive regression *** can overcome the weaknesses of widely used Gaussian process-based methods when faced with relatively high-dimensional design space or non-smooth patterns of objective *** simulation studies and real-world materials science case studies demonstrate their enhanced search efficiency and robustness.

关键词： functions properties optimization

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Sparse Additive Gaussian Process with Soft Interactions

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Open Journal of Statistics 2017年第4期7卷 567-588页

作者： Garret Vo debdeep pati Department of Industrial and Manufacturing Engineering Florida State University Tallahassee FL USA Department of Statistics Texas A and M University College Station TX USA

This paper presents a novel variable selection method in additive nonparametric regression model. This work is motivated by the need to select the number of nonparametric components and number of variables within each nonparametric component. The proposed method uses a combination of hard and soft shrinkages to separately control the number of additive components and the variables within each component. An efficient algorithm is developed to select the importance of variables and estimate the interaction network. Excellent performance is obtained in simulated and real data examples.

关键词： Additive Gaussian Process Interaction Lasso Sparsity Variable Selection

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