TY - GEN
T1 - Sparse recovery for clutter identification in radar measurements
AU - Kelsey, Malia
AU - Sen, Satyabrata
AU - Xiang, Yijian
AU - Nehorai, Arye
AU - Akcakaya, Murat
N1 - Funding Information:
Yijian Xiang, Murat Akcakaya and Arye Nehorai were supported by Air Force Office of Scientific Research (AFOSR) under award number FA9550-16-1-0386.
Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Anomaly Detection
KW - Cognitive Radar
KW - Nonhomogeneous Clutter
KW - Nonstationary Environments
KW - Orthogonal Matching Pursuit
KW - Sparse Recovery
UR - https://www.scopus.com/pages/publications/85021337442
U2 - 10.1117/12.2264090
DO - 10.1117/12.2264090
M3 - Conference contribution
AN - SCOPUS:85021337442
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Compressive Sensing VI
A2 - Ahmad, Fauzia
PB - SPIE
T2 - Compressive Sensing VI: From Diverse Modalities to Big Data Analytics 2017
Y2 - 12 April 2017 through 13 April 2017
ER -