Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments

Donald L. Hall, Ram Mohan Narayanan, David Marion Jenkins, Jr., Erik H. Lenzing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Advancements in wireless technology have led to an increased demand in the enhancement of wireless security, especially in indoor environments as GPS and cellular services degrade in performance. Recent developments in wireless security for indoor environments have focused mainly on developing radio frequency fingerprinting approaches through machine learning for device classification or localization. The work performed and discussed herein describes a developed system that can simultaneously perform device classification and localization in indoor environments using designed vector sensing antenna and artificial intelligence concepts. The devices evaluated are considered to be non-cooperative emitters that convey wideband code division multiple access (WCDMA) information found in universal mobile telecommunication systems (UMTS). However, the designed approaches can be extended to other protocols such as Global System for Mobile communications (GSM), Long-Term Evolution (LTE), and Code Division Multiple Access (CDMA). Device classification is performed in line-of-sight (LoS) scenarios with a developed vector sensor based on statistical features are extracted from the received power spectra and evaluated by two machine learning models, i.e. support vector machine (SVM) and weighted-K-nearest neighbor (WKNN). The final analysis experimentally validates the localization of the UMTS devices in an indoor environment by means of a comparison between dimensionally reduced features extracted from a short-time Fourier transform matrix along with three-dimensional received signal strength features, all acquired by the designed vector sensor antenna. Extension to other wireless protocols is assessed by evaluation of narrowband GSM signals for localization whilst being compared to the localization performance of the wideband UMTS non-cooperative emitters via WKNN.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXIII
EditorsKenneth I. Ranney, Armin Doerry
PublisherSPIE
ISBN (Electronic)9781510626713
DOIs
StatePublished - Jan 1 2019
EventRadar Sensor Technology XXIII 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11003
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRadar Sensor Technology XXIII 2019
CountryUnited States
CityBaltimore
Period4/15/194/17/19

Fingerprint

machine learning
learning
Learning systems
Machine Learning
emitters
Sensing
Mobile telecommunication systems
telecommunication
Telecommunications
code division multiple access
Global system for mobile communications
Mobile Communication
Code division multiple access
Code Division multiple Access
antennas
communication
Antenna
Nearest Neighbor
Antennas
broadband

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Hall, D. L., Narayanan, R. M., Jenkins, Jr., D. M., & Lenzing, E. H. (2019). Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments. In K. I. Ranney, & A. Doerry (Eds.), Radar Sensor Technology XXIII [1100310] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11003). SPIE. https://doi.org/10.1117/12.2519661
Hall, Donald L. ; Narayanan, Ram Mohan ; Jenkins, Jr., David Marion ; Lenzing, Erik H. / Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments. Radar Sensor Technology XXIII. editor / Kenneth I. Ranney ; Armin Doerry. SPIE, 2019. (Proceedings of SPIE - The International Society for Optical Engineering).
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Hall, DL, Narayanan, RM, Jenkins, Jr., DM & Lenzing, EH 2019, Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments. in KI Ranney & A Doerry (eds), Radar Sensor Technology XXIII., 1100310, Proceedings of SPIE - The International Society for Optical Engineering, vol. 11003, SPIE, Radar Sensor Technology XXIII 2019, Baltimore, United States, 4/15/19. https://doi.org/10.1117/12.2519661

Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments. / Hall, Donald L.; Narayanan, Ram Mohan; Jenkins, Jr., David Marion; Lenzing, Erik H.

Radar Sensor Technology XXIII. ed. / Kenneth I. Ranney; Armin Doerry. SPIE, 2019. 1100310 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11003).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hall DL, Narayanan RM, Jenkins, Jr. DM, Lenzing EH. Non-cooperative emitter classification and localization with vector sensing and machine learning in indoor environments. In Ranney KI, Doerry A, editors, Radar Sensor Technology XXIII. SPIE. 2019. 1100310. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2519661