The Need For Simultaneous Tracking And Recognition In Drone Surveillance Radar

Harman, Stephen; Ahmad, Bashar I.

doi:10.23919/irs51887.2021.9466222

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…An example block diagram is shown in Figure 1. Merging two or more of these tasks is known to substantially improve the radar performance against mUASs, for instance track-before-detect [10], joint tracking-classification [11] and even recognisebefore-detect. The latter however regularly refer to a rulebased filtering of detections to prevent overloading the multitarget tracker or the operator.…”

Section: Drone Surveillance Radar and Atcmentioning

confidence: 99%

A Review of Automatic Classification of Drones Using Radar: Key Considerations, Performance Evaluation, and Prospects

Ahmad,

Rogers,

Harman

et al. 2024

IEEE Aerosp. Electron. Syst. Mag.

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Automatic target classification is a critical capability for non-cooperative drone surveillance radars in several defence and civilian applications. It is accordingly a well-established research field and numerous algorithms exist for recognising targets, including miniature unmanned air systems (i.e., small, mini, micro and nano platforms), from their radar signatures. They have notably benefited from advances in machine learning (e.g., deep neural networks) and are increasingly able to achieve remarkably high accuracies. 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 outline the key challenges and considerations associated with the automatic classification of miniature drones from radar data. We then present a set of important performance metrics, from an end-user perspective. They are relevant to typical drone surveillance system requirements and constraints. Selected examples from real radar observations are shown for illustrations. We also outline here various emerging approaches and future directions that can produce more robust drone classifiers for radar.

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Section: Drone Surveillance Radar and Atcmentioning

confidence: 99%