Clutter identification based on kernel density estimation and sparse recovery

Haokun Wang; Yijian Xiang; Elise Dagois; Malia Kelsey; Satyabrata Sen; Arye Nehorai; Murat Akcakaya

doi:10.1117/12.2309320

14 May 2018 Clutter identification based on kernel density estimation and sparse recovery

Haokun Wang, Yijian Xiang, Elise Dagois, Malia Kelsey, Satyabrata Sen, Arye Nehorai, Murat Akcakaya

Proceedings Volume 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics; 106580G (2018) https://doi.org/10.1117/12.2309320
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

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.

Conference Presentation

Citation Download Citation

Haokun Wang, Yijian Xiang, Elise Dagois, Malia Kelsey, Satyabrata Sen, Arye Nehorai, and Murat Akcakaya "Clutter identification based on kernel density estimation and sparse recovery ", Proc. SPIE 10658, Compressive Sensing VII: From Diverse Modalities to Big Data Analytics, 106580G (14 May 2018); https://doi.org/10.1117/12.2309320

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