Maritime situational awareness using adaptive multi-sensor management under hazy conditions

Autor:	Prasad, D. K., Prasath, C. K., Rajan, D., Rachmawati, L., Rajabally, E., Quek, C.
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: 11 pages, 2 figures, MTEC 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1702.00754 Zobrazit plný text záznamu View this record from Arxiv