Approximate regularised maximum-likelihood approach for censoring outliers

Abstract

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.

Keywords

• 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