Compressive sensing kernel optimization for time delay estimation

Yujie Gu, Nathan A. Goodman

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

11 Scopus citations


The random projections usually adopted in compressive sensing applications do not exploit a priori knowledge of the sensing task or expected signal structure (other than the fundamental assumption of sparsity). In this paper, we use a task-specific information-based approach to optimizing the compressive sensing kernels for the time delay estimation of radar targets. The measurements are modeled according to a Gaussian mixture model by approximately discretizing the a priori distribution of the time delay. The sensing kernel that maximizes the Shannon mutual information between the measurements and the time delay is then approximated via a gradient-based approach. In addition, we also derive the Bayesian Cramér-Rao bound (CRB) on the time delay estimate as a function of the compressive sensing measurement kernels. Simulation results demonstrate that the proposed optimal sensing kernel outperforms random projections and the performance is consistent with the Bayesian CRB versus signal-to-noise ratio. We conclude that compressive sensing has potential utility in providing measurements with improved resolution for radar target parameter estimation problems.

Original languageEnglish
Title of host publication2014 IEEE Radar Conference
Subtitle of host publicationFrom Sensing to Information, RadarCon 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781479920341
StatePublished - 2014
Externally publishedYes
Event2014 IEEE Radar Conference, RadarCon 2014 - Cincinnati, OH, United States
Duration: 19 May 201423 May 2014

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2014 IEEE Radar Conference, RadarCon 2014
Country/TerritoryUnited States
CityCincinnati, OH


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