From Event: SPIE Commercial + Scientific Sensing and Imaging, 2017
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.
Malia Kelsey, Satyabrata Sen, Yijian Xiang, Arye Nehorai, and Murat Akcakaya, "Sparse recovery for clutter identification in radar measurements," Proc. SPIE 10211, Compressive Sensing VI: From Diverse Modalities to Big Data Analytics, 1021106 (Presented at SPIE Commercial + Scientific Sensing and Imaging: April 12, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2264090.
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