Acoustic Vessel Identification via Machine Learning
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Solution Overview
Problem
Existing systems for identifying maritime vessels without satellite tracking data are inadequate, particularly in situations where satellite transponders are rendered inoperative or unreliable, such as due to privacy concerns or illicit activities, leading to impaired monitoring and surveillance capabilities.
Innovation Solution
A machine learning model trained using satellite data is developed to recognize maritime vessels based on acoustic signatures collected by sensors deployed on water-based platforms, allowing for vessel identification even in the absence of satellite data, through supervised learning techniques and integration with data hubs for processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If satellite tracking data is used for vessel identification, then identification accuracy is improved, but system reliability deteriorates when transponders are disabled or spoofed
Solution Approach 1:
The patent introduces acoustic sensors as an intermediary detection method. Instead of directly relying on satellite transponder data, the system uses acoustic signatures detected by underwater sensors as a mediator to identify vessels. This indirect detection method maintains identification capability when direct satellite tracking is compromised.
Solution Approach 2:
The patent replaces the electronic/satellite-based identification system with an acoustic detection system. By substituting the mechanical/electronic transponder dependency with acoustic field-based detection, the system achieves alternative identification pathways that are not vulnerable to transponder disabling or spoofing.
2Adaptability or versatility
If acoustic sensors are deployed for vessel detection, then monitoring coverage is improved, but device complexity increases
Solution Approach 1:
The patent makes the acoustic sensor system multi-functional by integrating it with existing satellite data infrastructure and machine learning platforms. The same acoustic detection network serves multiple purposes: vessel identification, traffic monitoring, and surveillance, thereby justifying the complexity through enhanced versatility and coverage.
Solution Approach 2:
The patent creates a virtual model of vessel acoustic signatures through machine learning. Instead of deploying physical sensors everywhere, the system learns from acoustic data and creates digital twins or models of vessel signatures, which can then be used for identification without requiring continuous physical sensor deployment in all locations.
3Measurement precision
If machine learning models are trained with satellite data, then vessel recognition accuracy is improved, but loss of information occurs when satellite data is unavailable
Solution Approach 1:
The patent performs preliminary training of machine learning models using satellite data when it is available. The system proactively learns vessel acoustic signatures during periods of satellite data availability, storing this knowledge for later use when satellite data is unavailable, thus preventing information loss during critical periods.
Solution Approach 2:
The patent changes the data modality parameter from satellite-based optical/radar data to acoustic signal parameters. By transforming the identification problem from visual recognition to acoustic pattern recognition, the system maintains recognition accuracy using a different physical domain that remains available when satellite data is blocked or unavailable.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables persistent coverage and accurate identification of maritime vessels using acoustic data, enhancing monitoring capabilities and overcoming limitations of satellite data unavailability, including scenarios where transponders are disabled or spoofed.
Implementation Method 1
capturing sound vibrations traveling in water with an acoustic sensor, the sound vibrations produced from a water-based vessel
Data Source
AI summary
A machine learning model can be developed and deployed to detect a water-based vessel, such as a maritime vessel, using acoustic data collected from sensors structured to detect sound traveling in water caused by operation of the water-based vessel. Information received from satellite tracking of vessels (either transponder based signals or imagery of a body of water having the vessels) can be used as a label to train the machine learning model to discern acoustic signatures related to the labelled vessel. The acoustic data is collected from a water-based platform, such as a buoy, which includes one or more acoustic sensors. Other sensor types can also be used to train the data-based model on other aspects of the vessel, such as radar, infrared, electro-optical, and/or lidar. The buoys can be mounted to the seafloor or permitted to be free floating of the type that can either maneuver itself or be required to be repositioned from time to time. More than one buoy can be used to collect data.