Open AccessApproximate regularised maximum‐likelihood approach for censoring outliers
Author(s) -
Han Sudan,
De Maio Antonio,
Pallotta Luca,
Carotenuto Vincenzo,
Iommelli Salvatore,
Huang Xiaotao
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0717
Subject(s) - censoring (clinical trials) , outlier , computer science , maximum likelihood , sample (material) , data mining , artificial intelligence , mathematics , statistics , chemistry , chromatography
This study considers censoring outliers in a radar scenario with limited sample support. The problem is formulated as obtaining the regularised maximum likelihood (RML) estimate of the outlier index set. Since the RML estimate involves solving a combinatorial optimisation problem, a reduced complexity but approximate RML (ARML) procedure is also devised. As to the selection of the regularisation parameter, the cross‐validation technique is exploited. At the analysis stage, the performance of the RML/ARML procedure is evaluated based both on simulated and challenging knowledge‐aided sensor signal processing and expert reasoning data, also in comparison with some other outlier excision methods available in the open literature. The numerical results highlight that the RML/ARML algorithm achieves a satisfactory performance level in the presence of limited as well as sufficient sample supports whereas the other counterparts often experience a certain performance degradation for the insufficient training volume.