Maritime transport and vessel monitoring rely on multiple systems for positioning, such as the Automatic Identification System, electro-optical systems, and shore-based radar systems, to improve safety and efficiency in vessel tracking. However, each system has inherent limitations, including coverage gaps, reliance on vessel compliance, and limited real-time monitoring capabilities. As a complementary approach to existing methods and systems, this paper presents the SeaSentry system, a passive sensor network designed to detect, position, and track vessels in real time, thus eliminating the need for onboard installations. The sensors detect radar pulses emitted by the vessels' rotating radar antennas and compute time stamps as the radar beams pass over them. Geometric constraints can be derived from time differences of arrival to localize the vessels, with time error and synchronization demands in the millisecond range. Along with some initial results, this paper discusses the SeaSentry setup and data processing pipeline.

Assistance systems play an important role in the proceeding transition of surface vessels towards highly automated operations. Particularly when navigating through congested areas like harbors, exact knowledge of distances to nearby obstacles is essential for collision avoidance. This paper applies a combined filtering and clustering approach in order to utilize 1D FMCW radar data for distance estimation to nearby obstacles in the harbor environment. The data processing aims at clearing the raw sensor data from unwanted signals caused by environmental influences like rain or waves and determines a reliable distance from the relevant signals. We evaluate our approach using sea trial data from a research vessel, comparing processed radar distances with a DGPS-based ground truth. The study assesses the performance of three density-based clustering algorithms-DBSCAN, HDBSCAN, and OPTICS-in this context. All of these algorithms show a good performance for processing the 1D FMCW data for our use case, enabling a reliable distance determination to a static obstacle. OPTICS performs slightly better in terms of eliminating disturbing signals than the remaining two algorithms. The processing times of all algorithms were found to be sufficient for online application of the proposed approach.