Autonomous Maritime Vessel Sensor Data Fusion Architecture
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Solution Overview
Problem
The increasing number of diesel-electric submarines poses a challenge for naval forces, requiring advanced systems to locate and track these quiet vessels effectively, as existing manned surface ships lack the necessary precision, persistence, and flexibility for anti-submarine warfare operations in littoral regions.
Innovation Solution
The development of an autonomous maritime vehicle, ACTUV, equipped with a data fusion architecture and autonomy decision engine, utilizing Multi-Hypothesis Tracking (MHT) and Interacting Multiple Model (IMM) filters to process sensor data from various sources, enabling the vessel to autonomously identify and track maritime contacts, navigate, and avoid obstacles while adhering to maritime laws and conventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an autonomous maritime vehicle is deployed to locate and track submarines, then precision and persistence are improved, but device complexity increases
Solution Approach 1:
The autonomous maritime vehicle system is divided into distinct functional modules: sensor processing module for data acquisition, track processing module for target tracking, and autonomy decision engine for navigation decisions. This segmentation allows each module to specialize in specific tasks, improving overall measurement precision while managing complexity through modular architecture.
Solution Approach 2:
A data fusion architecture serves as an intermediary layer between multiple sensors and the autonomy decision engine. This intermediary processes and integrates data from diverse sensor sources (acoustic, magnetic, visual), enhancing detection precision for submarine tracking while abstracting the complexity of multi-sensor integration from the decision-making process.
2Measurement precision
If multiple sensor data sets are fused to determine operating environment state, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system merges multiple sensor data sets (acoustic, magnetic, visual, inertial) through a data fusion architecture that combines their complementary strengths. This merging improves measurement precision by cross-validating detections and reducing false positives, while the modular fusion architecture manages complexity through standardized integration protocols.
Solution Approach 2:
The autonomy decision engine is designed as a universal system that processes fused data from multiple sensor types to perform various functions: submarine detection, surface vessel tracking, collision avoidance, and navigation. This multi-functionality improves measurement precision across different operational scenarios while the standardized processing framework manages the inherent complexity.
3Reliability
If autonomous navigation with obstacle avoidance is implemented, then reliability is improved, but device complexity increases
Solution Approach 1:
The autonomy decision engine implements continuous feedback loops where sensor data is constantly processed, navigation decisions are executed, and outcomes are monitored. This feedback mechanism improves reliability by enabling real-time obstacle detection and avoidance while adjusting course to maintain mission objectives, with the modular architecture managing the complexity of continuous monitoring and adjustment.
Solution Approach 2:
The system performs preliminary actions by pre-processing sensor data to identify potential obstacles and navigation hazards before they become critical threats. The track processing module maintains predicted tracks of detected vessels, allowing the autonomy decision engine to plan avoidance maneuvers in advance, improving reliability while the staged processing manages computational complexity.
Data Source
AI summary
A system of modular components can be used with existing sensor suites to fuse data and determine the operating environment (surface contacts/tracks) for an autonomous marine vehicle and feed an autonomy decision engine to improve the vessel arbitration process in deciding which way to turn, how fast to go, obstacle avoidance, and mission monitoring. The system includes the ability to obey the set of navigation rules published by the International Maritime Organization, generally referred to as COLREGS (collision regulations).