Approximate regularised maximum-likelihood approach for censoring outliers

The Journal of Engineering

Salvatore Iommelli¹  Xiaotao Huang²  Sudan Han²  Antonio De Maio³  Vincenzo Carotenuto³  Luca Pallotta³ 

[1] Ente di Formazione Professionale Maxwell;National University of Defense Technology;Università degli Studi di Napoli Federico II’;

关键词: maximum likelihood estimation;    optimisation;    radar signal processing;    combinatorial mathematics;    communication complexity;    approximation theory;    radar receivers;    radar scenario;    combinatorial optimisation problem;    cross-validation technique;    knowledge-aided sensor signal processing;    expert reasoning data;    outlier excision methods;    satisfactory performance level;    approximate rml estimation procedure;    censoring outlier index set;    approximate regularised maximum-likelihood estimation approach;    reduced complexity;    arml procedure;   

DOI  :  10.1049/joe.2019.0717

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown