Information-theoretic compressive measurement for frequency hopping pattern recognition

Yujie Gu; Nathan A. Goodman

doi:10.1109/radar.2018.8378777

Information-theoretic compressive measurement for frequency hopping pattern recognition

Yujie Gu, Nathan A. Goodman

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

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 language	English
Title of host publication	2018 IEEE Radar Conference, RadarConf 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1445-1449
Number of pages	5
ISBN (Electronic)	9781538641675
DOIs	https://doi.org/10.1109/radar.2018.8378777
State	Published - 8 Jun 2018
Externally published	Yes
Event	2018 IEEE Radar Conference, RadarConf 2018 - Oklahoma City, United States Duration: 23 Apr 2018 → 27 Apr 2018

Publication series

Name	2018 IEEE Radar Conference, RadarConf 2018

Conference

Conference	2018 IEEE Radar Conference, RadarConf 2018
Country/Territory	United States
City	Oklahoma City
Period	23/04/18 → 27/04/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Funding

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

Funders	Funder number
Defense Advanced Research Projects Agency	66001-10-1-4079

Keywords

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

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10.1109/radar.2018.8378777

Cite this

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title = "Information-theoretic compressive measurement for frequency hopping pattern recognition",

abstract = "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.",

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doi = "10.1109/radar.2018.8378777",

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Gu, Y & Goodman, NA 2018, Information-theoretic compressive measurement for frequency hopping pattern recognition. in 2018 IEEE Radar Conference, RadarConf 2018. 2018 IEEE Radar Conference, RadarConf 2018, Institute of Electrical and Electronics Engineers Inc., pp. 1445-1449, 2018 IEEE Radar Conference, RadarConf 2018, Oklahoma City, United States, 23/04/18. https://doi.org/10.1109/radar.2018.8378777

Information-theoretic compressive measurement for frequency hopping pattern recognition. / Gu, Yujie; Goodman, Nathan A.
2018 IEEE Radar Conference, RadarConf 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1445-1449 (2018 IEEE Radar Conference, RadarConf 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Information-theoretic compressive measurement for frequency hopping pattern recognition

Abstract

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Keywords

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