An Approximate Regularized ML Approach to Censor Outliers in Gaussian Radar Data

An Approximate Regularized ML Approach to Censor Outliers in Gaussian Radar Data

This paper considers the problem of censoring outliers from the secondary dataset in a radar scenario where the sample support is limited. To this end, the generalized regularized likelihood function (GRLF) criterion is used and the corresponding regularized maximum likelihood (RML) estimate of the...

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Bibliographic Details
Main Authors: Sudan Han, Luca Pallotta, Vincenzo Carotenuto, Antonio De Maio, Xiaotao Huang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Outlier removal
regularized ML
limited training data
expected likelihood
cross-validation
Online Access:https://ieeexplore.ieee.org/document/8718591/

Internet

https://ieeexplore.ieee.org/document/8718591/

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