Sample selection and adaptive weight allocation for compressive MIMO UWB noise radar

Yangsoo Kwon, Ram M. Narayanan, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we propose a sample selection method for compressive multiple-input multiple-output (MIMO) ultra-wideband (UWB) noise radar imaging. The proposed sample selection is based on comparing norm values of the transmitted sequences, and selects the largest M samples among N candidates per antenna. Moreover, we propose an adaptive weight allocation which improves normalized mean-square error (NMSE) by maximizing the mutual information between target echoes and the transmitted signals. Further, this weighting scheme is applicable to both sample selection schemes, a conventional random sampling and the proposed selection. Simulations show that the proposed selection method can improve the multiple target detection probability and NMSE. Moreover, the proposed weight allocation scheme is applicable to those selection methods and obtains spatial diversity and signal-to-noise ratio (SNR) gains.

Original languageEnglish (US)
Title of host publicationCompressive Sensing
StatePublished - 2012
EventCompressive Sensing - Baltimore, MD, United States
Duration: Apr 26 2012Apr 27 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


OtherCompressive Sensing
Country/TerritoryUnited States
CityBaltimore, MD

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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