Multi-stage compressed sensing and wall clutter mitigation for through - The-wall radar image formation

Fok Hing Chi Tivive, Abdesselam Bouzerdoum, Van Ha Tang

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

6 Citations (Scopus)

Abstract

In this paper, a multi-stage through - The-wall radar imaging technique combining wall clutter mitigation and scene reconstruction is proposed. In the first stage, compressed sensing is applied to compressive measurements to recover the radar signals in the wavelet domain. Then, a subspace projection method is employed to remove the wavelet coefficients associated with the exterior wall reflections. In the second stage, the remaining wavelet coefficients are further compressed using principal component analysis. A compact linear measurement model is then formulated which relates the compressed wavelet coefficients to the image of the scene. Finally, the image reconstruction problem is solved in a more efficient compressed sensing framework, using the compact linear measurement model. Experiment results obtained from real data prove that the proposed method is more efficient and achieves better performance, in terms of target-to-clutter ratio, than direct compressed sensing signal recovery method and delay-and-sum beamforming.

Original languageEnglish
Title of host publication2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
PublisherIEEE Computer Society
Pages489-492
Number of pages4
ISBN (Print)9781479914814
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014 - A Coruna, Spain
Duration: 22 Jun 201425 Jun 2014

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
ISSN (Electronic)2151-870X

Conference

Conference2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop, SAM 2014
Country/TerritorySpain
CityA Coruna
Period22/06/1425/06/14

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