Impacts of hardware nonlinearities on compressive sensing performance

Yujie Gu, Nathan A. Goodman

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

1 Scopus citations

Abstract

In previous work, we have exploited prior knowledge of target signals to design compressive measurement kernels for fast-time sub-Nyquist sampling. The kernels were designed to maximize the Shannon mutual information between the measurement and the target signals to be estimated. We showed that in cases where the radar system is resolution-limited rather than noise-limited, compressive sensing (CS) could provide a performance benefit for these applications, despite the signal-to-noise ratio (SNR) loss inherent in radio frequency compressive sensing. Hence, the largest performance gains were seen at high SNR. On the other hand, both the kernel optimization and the signal reconstruction are model based, meaning they are highly dependent on an accurate forward model of the sensing process. Because sub-Nyquist analog-to-digital conversion requires analog multiplication and lowpass filtering, the forward model must accurately quantify the signal operations in order for CS-based techniques to reach their full utility. In this paper, we model inaccuracies in the analog multiplier via third-order non-linear terms in the sensing model and quantify their effect on overall performance.

Original languageEnglish
Title of host publication2018 IEEE Radar Conference, RadarConf 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1579-1583
Number of pages5
ISBN (Electronic)9781538641675
DOIs
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

Conference

Conference2018 IEEE Radar Conference, RadarConf 2018
Country/TerritoryUnited States
CityOklahoma City
Period23/04/1827/04/18

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.

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

    Keywords

    • Compressive measurement
    • hardware implementation
    • nonlinearity

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