5 May 2017 Sparse recovery for clutter identification in radar measurements
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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.
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Malia Kelsey, Malia Kelsey, Satyabrata Sen, Satyabrata Sen, Yijian Xiang, Yijian Xiang, Arye Nehorai, Arye Nehorai, Murat Akcakaya, Murat Akcakaya, } "Sparse recovery for clutter identification in radar measurements", Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021106 (5 May 2017); doi: 10.1117/12.2264090; https://doi.org/10.1117/12.2264090
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