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Title: Clutter identification based on kernel density estimation and sparse recovery

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.

Authors:
 [1];  [2];  [1];  [1]; ORCiD logo [3];  [2];  [1]
  1. University of Pittsburgh
  2. Washington University, St. Louis
  3. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1439142
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: SPIE Defense and Commercial Sensing (DCS 2018) - Orlando, Florida, United States of America - 4/15/2018 8:00:00 AM-4/19/2018 8:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Wang, Haokun, Xiang, Yijian, Dagois, Elise, Kelsey, Malia, Sen, Satyabrata, Nehorai, Arye, and Akcakaya, Murat. Clutter identification based on kernel density estimation and sparse recovery. United States: N. p., 2018. Web.
Wang, Haokun, Xiang, Yijian, Dagois, Elise, Kelsey, Malia, Sen, Satyabrata, Nehorai, Arye, & Akcakaya, Murat. Clutter identification based on kernel density estimation and sparse recovery. United States.
Wang, Haokun, Xiang, Yijian, Dagois, Elise, Kelsey, Malia, Sen, Satyabrata, Nehorai, Arye, and Akcakaya, Murat. Tue . "Clutter identification based on kernel density estimation and sparse recovery". United States. https://www.osti.gov/servlets/purl/1439142.
@article{osti_1439142,
title = {Clutter identification based on kernel density estimation and sparse recovery},
author = {Wang, Haokun and Xiang, Yijian and Dagois, Elise and Kelsey, Malia and Sen, Satyabrata and Nehorai, Arye and Akcakaya, Murat},
abstractNote = {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.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {5}
}

Conference:
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