Autonomous Radar Ornithology: Benchmarking Automated Bird Detection and Tracking

Automated radar-based tracking offers a scalable solution for avian monitoring, particularly in remote or data-intensive settings where manual annotation is impractical. This study evaluates the performance of the GlobAl Nearest Neighbour targEt Tracker (GANNET), a low-cost and customisable algorithm for detecting and tracking bird-like targets in X-band marine radar imagery. Using data from the Fall of Warness tidal test site in Orkney, Scotland, we compared manual and automated outputs across more than 55,000 filtered scan-level detections and over 34,000 consolidated trajectories derived from good-quality imagery. Trajectories were defined using a minimum track-length threshold of six detections, with a mean of 22 detections per track. GANNET processed data over 10 times faster than manual annotation and showed higher sensitivity, particularly in medium- and low-quality imagery where manual performance deteriorated. These results highlight GANNET's value for retrospective analysis of archived radar datasets and its potential application in environmental impact monitoring for birds in marine renewable energy contexts. Targeted improvements in clutter suppression, motion modelling and validation against a cooperative target would further strengthen its suitability for operational deployment. GANNET therefore represents a promising foundation for next-generation radar ornithology and offshore biodiversity assessment.