TY - GEN
T1 - Clutter identification based on sparse recovery and L1-type probabilistic distance measures
AU - Zhu, Yuansheng
AU - Xiang, Yijian
AU - Sen, Satyabrata
AU - Dagois, Elise
AU - Nehorai, Arye
AU - Akcakaya, Murat
N1 - Publisher Copyright:
© 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2020/5
Y1 - 2020/5
N2 - Cognitive radar framework has recently been proposed in radar signal processing to develope algorithms for target detection, tracking, and waveform design in the presence of nonstationary environmental (clutter) characteristics. In this framework, there are the three main steps: sensing the environmental changes, learning the new environmental statistical characteristics, and adapting the radar algorithms to the new characteristics. Here, we focus on the second step of the framework to identify the new clutter characteristics after a change is detected in the environment. We form a dictionary of various clutter distributions and identify the distribution of the new clutter data through matching pursuit using probabilistic similarity and distance measures under sparsity constraints. Specifically, we use inner-product as a similarity measure, and we apply three different L1-norm type probabilistic distance measures. We both numerically and analytically analyze their clutter-distribution identification performances and show that Kulczynski is the best distance measure for distribution identification.
AB - Cognitive radar framework has recently been proposed in radar signal processing to develope algorithms for target detection, tracking, and waveform design in the presence of nonstationary environmental (clutter) characteristics. In this framework, there are the three main steps: sensing the environmental changes, learning the new environmental statistical characteristics, and adapting the radar algorithms to the new characteristics. Here, we focus on the second step of the framework to identify the new clutter characteristics after a change is detected in the environment. We form a dictionary of various clutter distributions and identify the distribution of the new clutter data through matching pursuit using probabilistic similarity and distance measures under sparsity constraints. Specifically, we use inner-product as a similarity measure, and we apply three different L1-norm type probabilistic distance measures. We both numerically and analytically analyze their clutter-distribution identification performances and show that Kulczynski is the best distance measure for distribution identification.
KW - Cognitive Radar
KW - Kernel Density Estimation
KW - Orthogonal Matching Pursuit
KW - Probabilistic Similarity and Distance Measures
KW - Sparse Recovery
UR - https://www.scopus.com/pages/publications/85091322336
U2 - 10.1109/ICASSP40776.2020.9054669
DO - 10.1109/ICASSP40776.2020.9054669
M3 - Conference contribution
AN - SCOPUS:85091322336
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4772
EP - 4776
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -