Marine Proximity Sensing with Most Important Object Filtering
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Solution Overview
Problem
Existing proximity sensing systems for marine vessels generate large amounts of data impractical for embedded controllers and bandwidth-limited networks, with insufficient data filtering and prioritization methods for autonomous or semi-autonomous navigation.
Innovation Solution
A simplified two-dimensional vessel outline is used to calculate and prioritize proximity measurements, reducing computational load by identifying the most important object dataset, which includes linearly and rotationally closest measurements, and translating data into a common reference frame for navigation control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive proximity measurements from multiple sensors are collected for autonomous navigation, then navigation safety and collision avoidance are improved, but data processing complexity and computational load increase
Solution Approach 1:
The patent extracts only the most critical proximity measurements from the complete sensor dataset by identifying objects in critical zones (collision course, close proximity, and restricted area objects). This selective extraction reduces the data volume processed by embedded controllers while maintaining navigation safety, as only these critical objects require immediate attention for collision avoidance.
Solution Approach 2:
The patent applies local quality by differentiating data processing priorities based on spatial location and collision risk. Critical zone objects receive high-priority processing with detailed analysis, while non-critical objects use simplified processing. This localized approach optimizes computational resources by allocating processing power where it is most needed for safety-critical decisions.
2Measurement precision
If detailed proximity data from all sensors is processed, then measurement precision is improved, but bandwidth consumption and network load increase
Solution Approach 1:
The patent extracts essential proximity parameters (distance, bearing, closing speed, time to impact) only for objects in critical zones, rather than transmitting complete sensor datasets. This selective data extraction maintains measurement precision for safety-critical objects while significantly reducing network bandwidth consumption for autonomous navigation communications.
3Reliability
If multiple proximity sensors are deployed around the vessel, then collision detection coverage is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the operational environment into distinct spatial zones (critical zone, restricted area, and general surveillance areas) with different processing priorities. This segmentation allows the system to manage multiple sensor inputs efficiently by applying different processing strategies to different zones, reducing overall system complexity while maintaining comprehensive collision detection coverage.
4Speed
If real-time processing of all proximity measurements is performed, then response time for collision avoidance is improved, but computational load on embedded controllers increases
Solution Approach 1:
The patent extracts critical time-sensitive parameters (closing speed, time to impact, range rate) only for objects in critical zones requiring immediate response. This selective extraction enables real-time processing on embedded controllers with limited computational power, as the system focuses computational resources on the most time-critical collision avoidance decisions rather than processing all sensor data at full speed.
Data Source
AI summary
A proximity sensor system on a marine vessel includes one or more proximity sensors, each at a sensor location on the marine vessel and configured to measure proximity of objects and generate proximity measurements. A processor is configured to store a two-dimensional vessel outline of the marine vessel with respect to a point of navigation for the marine vessel, receive the proximity measurements measured by one or more proximity sensors on the marine vessel, and identify four linearly-closest proximity measurements to the two-dimensional vessel outline, including one closest proximity measurement in each of a positive X direction, a negative X direction, a positive Y direction, and a negative Y direction. The processor then generates a most important object (MIO) dataset identifying the four linearly-closest proximity measurements.