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Proceedings Paper

Sparse recovery for clutter identification in radar measurements
Author(s): Malia Kelsey; Satyabrata Sen; Yijian Xiang; Arye Nehorai; Murat Akcakaya
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Paper Abstract

Most existing radar algorithms are developed under the assumption that the environment, data clutter, is known and stationary. However, in practice, the characteristics of clutter can vary enormously in time depending on the operational scenarios. If unaccounted for, these nonstationary variabilities may drastically hinder the radar performance. It is essential that the radar systems dynamically detect changes in the environment, and adapt to these changes by learning the new statistical characteristics of the environment. In this paper, we employ sparse recovery for clutter identification, specifically we identify the statistical profile the clutter follows. We use Monte Carlo simulations to simulate and test clutter data coming from various distributions.

Paper Details

Date Published: 5 May 2017
PDF: 10 pages
Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021106 (5 May 2017); doi: 10.1117/12.2264090
Show Author Affiliations
Malia Kelsey, Univ. of Pittsburgh (United States)
Satyabrata Sen, Oak Ridge National Lab. (United States)
Yijian Xiang, Washington Univ. in St. Louis (United States)
Arye Nehorai, Washington Univ. in St. Louis (United States)
Murat Akcakaya, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 10211:
Compressive Sensing VI: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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