Transformer-Based Multitarget Bernoulli Tracking for Maritime Radar

Multi-target tracking in the maritime domain is a challenging problem due to the non-Gaussian and fluctuating characteristics of sea clutter. This article investigates the use of machine learning (ML) to detect and track weak targets in the maritime domain. The proposed method uses a transformer to extract point measurements from range-azimuth maps, before clustering and tracking using the Labelled multi-Bernoulli (LMB) filter. A birth density design using the transformer attention maps and prediction scores is also developed. The error performance of the transformer based approach is presented and compared with a constant false alarm rate (CFAR) detection technique. The LMB filter is run in two scenarios, an ideal birth approach, and the measurement driven birth approach. Experiments indicate that the transformer based method has superior performance to the CFAR approach for all target scenarios discussed.