Information-theoretic compressive measurement for frequency hopping pattern recognition

Yujie Gu, Nathan A. Goodman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations


In this paper, we propose an information-theoretic compressive measurement scheme for the frequency hopping pattern recognition of frequency-hopping spread spectrum (FHSS) signals. We model the FHSS signals as a Gaussian mixture distribution, where each component is corresponding to one candidate frequency hopping channel. The compressive measurement scheme is then designed to maximize the Shannon mutual information between the compressive measurements and the frequency hopping pattern to be recognized. The approximated mutual information gradient with respect to the compressive measurement matrix is used in a gradient-based approach to search for the optimal compressive measurement scheme. Simulation results demonstrate that the proposed information-theoretic compressive measurement scheme can significantly improve the recognition performance of the frequency hopping pattern than random projections typically used in the classical compressive sensing theory.

Original languageEnglish
Title of host publication2018 IEEE Radar Conference, RadarConf 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781538641675
StatePublished - 8 Jun 2018
Externally publishedYes
Event2018 IEEE Radar Conference, RadarConf 2018 - Oklahoma City, United States
Duration: 23 Apr 201827 Apr 2018

Publication series

Name2018 IEEE Radar Conference, RadarConf 2018


Conference2018 IEEE Radar Conference, RadarConf 2018
Country/TerritoryUnited States
CityOklahoma City

Bibliographical note

Publisher Copyright:
© 2018 IEEE.


We gratefully acknowledge support from the Defense Advanced Research Projects Agency (DARPA) via grant #N66001-10-1-4079.

FundersFunder number
Defense Advanced Research Projects Agency66001-10-1-4079


    • Compressive measurement
    • Gaussian mixture (GM)
    • frequency-hopping spread spectrum (FHSS)
    • mutual information
    • pattern recognition


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