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

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.

Paper Details

Date Published: 3 May 2019
PDF: 9 pages
Proc. SPIE 11003, Radar Sensor Technology XXIII, 110031C (3 May 2019); doi: 10.1117/12.2522428
Show Author Affiliations
Zacharie Idriss, The Pennsylvania State Univ. (United States)
U.S. Naval Research Lab. (United States)
Raghu G. Raj, U.S. Naval Research Lab. (United States)
Ram M. Narayanan, The Pennsylvania State Univ. (United States)


Published in SPIE Proceedings Vol. 11003:
Radar Sensor Technology XXIII
Kenneth I. Ranney; Armin Doerry, Editor(s)

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