Maritime situational awareness using adaptive multi-sensor management under hazy conditions
This addresses navigation challenges for autonomous maritime vessels in poor visibility, but it appears incremental as it builds on existing sensor and data integration approaches.
The paper tackles the problem of autonomous maritime vessel navigation in hazy conditions by proposing a multi-sensor architecture with adaptive management, which combines on-board and external data to determine actions using computational intelligence and live learning, aiming to enhance autonomy in diverse weather.
This paper presents a multi-sensor architecture with an adaptive multi-sensor management system suitable for control and navigation of autonomous maritime vessels in hazy and poor-visibility conditions. This architecture resides in the autonomous maritime vessels. It augments the data from on-board imaging sensors and weather sensors with the AIS data and weather data from sensors on other vessels and the on-shore vessel traffic surveillance system. The combined data is analyzed using computational intelligence and data analytics to determine suitable course of action while utilizing historically learnt knowledge and performing live learning from the current situation. Such framework is expected to be useful in diverse weather conditions and shall be a useful architecture to provide autonomy to maritime vessels.