Occupancy grid based environment sensing for MASS in complex waters

To enable autonomous operation for Maritime Autonomous Surface Ships (MASS), a reliable environment sensing algorithm is required. This is because MASS may operate in complex waters with large numbers of static and dynamic objects, and environment data acquired from either onboard sensors or external information providing systems is subject to both random errors and reliability issue in harsh weather. Many existing marine radar target tracking algorithms implicitly assume the environment only consists of dynamic objects and ignore the existence of static objects with various structure and size, limiting their ability in utilizing knowledge from third parties such as electronic navigation chart. In this paper, an occupancy grid model (OGM) is constructed as a fundamental environment sensing framework. Mapping sensor data to occupancy grids can facilitate identification and inference on static objects as well as dynamic targets. Hidden Markov models (HMMs) are then introduced for tracking of moving vessels among discrete grids. The proposed algorithm employs a hierarchical structure to make use of both ENC and marine radar data for environment sensing. Experiment validation based on both simulation and real data shows that it is capable of identifying static objects and simultaneously tracking moving vessels using occupancy grids.