A compound Gaussian-based waveform design approach for enhanced target detection in multistatic radar imaging

Zacharie Idriss, Raghu G. Raj, Ram M. Narayanan

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

1 Scopus citations


Much work has been done designing transmit waveforms for target identification, classification, and detection. In addition, these have also been studied in both single and multiple-antenna scenarios. In this work, we study the construction of a waveform when multiple radar sensors are used to image a target scene. The scene is assumed to have a prior distribution given by a Compound Gaussian (CG) - a model that has proven very useful in the field of image processing. Waveform optimization is done with the objective of optimizing mutual information, while reconstruction was performed using sparsity based reconstruction techniques. In our work, the waveform is tailored for a particular target of interest in the scene while suppressing the clutter. Using our waveform techniques, we demonstrate statistically significant improvements in the quality of the reconstructed image in peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). We validate our algorithms using the MSTAR database.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXIII
EditorsKenneth I. Ranney, Armin Doerry
ISBN (Electronic)9781510626713
StatePublished - 2019
EventRadar Sensor Technology XXIII 2019 - Baltimore, United States
Duration: Apr 15 2019Apr 17 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceRadar Sensor Technology XXIII 2019
Country/TerritoryUnited States

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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