Utilizing 1D FMCW Radar Data for Distance Estimation to Port Infrastructure

Abstract

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.