Active Learning for Radar System Performance Verification
conference paper
This paper studies active learning methods for the verification of radar systems. Verification is based on evaluating the system for varying parameters-observation pairs. To reduce the number of observations, an evaluation-learning-selection cycle is introduced for radar systems aimed at reducing the number of samples by on-the-fly sampling. Emphasis is given to define a framework in which various strategies can be used to select the next-sample on-the-fly. The framework subdivides the sampling domain into subdomains to select the next sample, gauging discrepancy levels within these areas to guide the selection of subsequent parameters. Additionally, we proposed a set of uncertainty-quantifier functions for the various regression methods employed in the learning stage. By comparing these methods using a radar detection performance example, the competitiveness of cost-effective approaches in adaptive sampling for the verification of the radar system performance is illustrated.
TNO Identifier
994024
Publisher
IEEE
Source title
2024 IEEE Radar Conference (RadarConf24) 6 – 10 May 2024
Files
To receive the publication files, please send an e-mail request to TNO Repository.