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Proceedings Paper

Clutter identification based on kernel density estimation and sparse recovery
Author(s): Haokun Wang; Yijian Xiang; Elise Dagois; Malia Kelsey; Satyabrata Sen; Arye Nehorai; Murat Akcakaya
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Paper Abstract

A cognitive radar framework is being developed to dynamically detect changes in the clutter characteristics, and to adapt to these changes by identifying the new clutter distribution. In our previous work, we have presented a sparse-recovery based clutter identification technique. In this technique, each column of the dictionary represents a specific distribution. More specifically, calibration radar clutter data corresponding to a specific distribution is transformed into a distribution through kernel density estimation. When the new batch of radar data arrives, the new data is transformed to a distribution through the same kernel density estimation method and its distribution characteristics is identified through sparse-recovery. In this paper, we extend our previous work to consider different kernels and kernel parameters for sparse-recovery-based clutter identification and the numerical results are presented as well. The impact of different kernels and kernel parameters are analyzed by comparing the identification accuracy of each scenario.

Paper Details

Date Published: 14 May 2018
PDF: 9 pages
Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 106580G (14 May 2018); doi: 10.1117/12.2309320
Show Author Affiliations
Haokun Wang, Univ. of Pittsburgh (United States)
Yijian Xiang, Washington Univ. in St. Louis (United States)
Elise Dagois, Univ. of Pittsburgh (United States)
Malia Kelsey, Univ. of Pittsburgh (United States)
Satyabrata Sen, Oak Ridge National Lab. (United States)
Arye Nehorai, Washington Univ. in St. Louis (United States)
Murat Akcakaya, Univ. of Pittsburgh (United States)

Published in SPIE Proceedings Vol. 10658:
Compressive Sensing VII: From Diverse Modalities to Big Data Analytics
Fauzia Ahmad, Editor(s)

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