Range and Velocity Estimation Using Kernel Maximum Correntropy Based Nonlinear Estimators in Non-Gaussian Clutter

In this article, we propose kernel maximum correntropy based nonlinear estimators for range and velocity estimation in non-Gaussian clutter and system nonlinearity. The proposed estimators are analyzed for linear frequency modulated and stepped frequency radar systems. Additionally, an adaptive upda...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems 2020-06, Vol.56 (3), p.1992-2004
Main Authors: Singh, Uday Kumar, Mitra, Rangeet, Bhatia, Vimal, Mishra, Amit Kumar
Format: Article
Language:English
Subjects:
Adaptive systems
Clutter
Delay
Delays
Doppler radar
Doppler shift
Estimation
Estimators
Fisher information
kernel maximum correntropy (KMC)
Kernels
linear frequency modulated (LFM)
Lower bounds
Nonlinearity
Optimization
Performance evaluation
Radar cross-sections
Radar equipment
stepped frequency (SF)
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Summary:In this article, we propose kernel maximum correntropy based nonlinear estimators for range and velocity estimation in non-Gaussian clutter and system nonlinearity. The proposed estimators are analyzed for linear frequency modulated and stepped frequency radar systems. Additionally, an adaptive update equation is derived for optimization of the kernel width, which further lowers the dictionary size and the variance of the proposed estimators. For performance evaluation of the proposed estimators, an expression is derived for the Cramer-Rao lower bound using a modified Fisher information matrix.
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2019.2948518