A Review of Automatic Classification of Drones Using Radar: Key
Considerations, Performance Evaluation and Prospects
Abstract
Automatic target classification or recognition is a critical capability
in non-cooperative surveillance with radar in several defence and
civilian applications. It is a well-established research field and
numerous techniques exist for recognising targets, including miniature
unmanned air systems or drones (i.e., small, mini, micro and nano
platforms), from their radar signatures. These algorithms have notably
benefited from advances in machine learning (e.g., deep neural networks)
and are increasingly able to achieve remarkably high accuracy. Such
classification results are often captured by standard, generic, object
recognition metrics and originate from testing on simulated or real
radar measurements of drones under high signal to noise ratios. Hence,
it is difficult to assess and benchmark the performance of different
classifiers under realistic operational conditions. In this paper, we
first review the key challenges and considerations associated with the
automatic classification of miniature drones from radar data. We then
present a set of important performance measures, from an end-user
perspective. These are relevant to typical drone surveillance system
requirements and constraints. Selected examples from real radar
observations are shown for illustration. We also outline here various
emerging approaches and future directions that can produce more robust
drone classifiers for radar.