In the automotive community, methods for tracking, localization,and situational awareness are routinely tested on well-known open-source datasets from the real world. In many other applications of target tracking, such as maritime radar tracking, there is a lack of such data. In this paper, we present a large dataset consisting of data recorded by a frequency-modulated continuous wave radar overlooking the Trondheim City Canal over several weeks during the summer of 2023. The dataset includes a rich variety of boat traffic, ranging from large ferries to formations of kayaks. All the data have been analyzed by means of classical joint integrated probabilistic data association-based multiple target tracking. We point out several challenges that arise in this dataset, such as merged measurements and multipath. We also demonstrate that the data are sufficient to generate statistical information about traffic patterns in the City Canal.

The detection of small and dim targets under low signal-to-noise ratio (SNR) circumstances is a commonly encountered yet challenging endeavour in radar signal processing. The standard approach to deal with undesirable background conditions involves coherent processing and integration with subsequent detection directly applied to the radar signals. However, optical flow, a widespread visual tracking method, has rarely been used in this context. In this paper, we address the issue of radar target detection in low SNR scenarios by employing optical flow on radar images. This work focuses on the divergence of the optical flow vector field, utilising a novel approach of coherently integrating consecutive flow fields calculated against a homogeneous reference plane. The proposed methodology allows for more robust target identification and thus a precise initialisation of tracking systems. To validate and demonstrate the benefits of the proposed approach, simulations are conducted and discussed.