این متن را ترجمه به فارسی کن با توجه به اینکه متن علمی می باشد

Abstract— Autonomous ships are expected to improve the level
of safety and efficiency in future maritime navigation. Such
vessels need perception for two purposes: to perform autonomous
situational awareness and to monitor the integrity of the sensor
system itself. In order to meet these needs, the perception
system must fuse data from novel and traditional perception
sensors using Artificial Intelligence (AI) techniques. This article
overviews the recognized operational requirements that are
imposed on regular and autonomous seafaring vessels, and then
proceeds to consider suitable sensors and relevant AI techniques
for an operational sensor system. The integration of four sensors
families is considered: sensors for precise absolute positioning
(Global Navigation Satellite System (GNSS) receivers and Inertial
Measurement Unit (IMU)), visual sensors (monocular and stereo
cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary
data, such as Automatic Identification System (AIS) and external
data archives are discussed. The perception tasks are related to
well-defined problems, such as situational abnormality detection,
vessel classification, and localization, that are solvable using AI
techniques. Machine learning methods, such as deep learning
and Gaussian processes, are identified to be especially relevant
for these problems. The different sensors and AI techniques
are characterized keeping in view the operational requirements,
and some example state-of-the-art options are compared based
on accuracy, complexity, required resources, compatibility and
adaptability to maritime environment, and especially towards
practical realization of autonomous systems.

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