Multi-Satellite Vessel Tracking via Predictive Observation Windows
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Solution Overview
Problem
Current methods for tracking traveling vessels are limited in accuracy and efficiency, particularly in predicting future locations and optimizing satellite imagery resource allocation, leading to high costs and inadequate coverage in large maritime areas.
Innovation Solution
A computer-implemented method and system that predicts possible future locations of a traveling vessel using historical navigation data and movement graphs, identifying optimal satellite observation windows for detection based on probability scores, and repeatedly adjusts predictions if the vessel is not detected, utilizing low Earth orbiting satellites and AIS data for precise tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite imagery resources are allocated broadly to cover large maritime areas, then coverage detection is improved, but costs increase
Solution Approach 1:
The system performs preliminary actions by predicting future vessel locations using historical navigation data and movement graphs before actual detection occurs. This allows the system to pre-identify high-probability detection windows and allocate satellite resources accordingly, avoiding costly broad coverage while ensuring effective monitoring at predicted locations.
Solution Approach 2:
Instead of uniformly distributing satellite coverage across entire maritime areas, the system applies local quality by concentrating resources on specific locations and time windows where vessel detection probability is highest. The detection probability score identifies optimal local areas for satellite pass, enabling cost-effective targeted monitoring rather than diffuse coverage.
2Reliability
If multiple satellites are used to improve detection coverage, then tracking reliability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex multi-satellite tracking problem into manageable components: predicting vessel movement, calculating detection probability scores, identifying optimal observation windows, and selecting specific satellites for each segment. This segmentation reduces overall system complexity by breaking down the coordination of multiple satellites into independent, manageable tasks.
Solution Approach 2:
The system implements feedback mechanisms where detection outcomes are fed back into the system to refine future predictions. When a vessel is detected or not detected, this information updates the movement graphs and prediction models, improving future detection reliability while maintaining manageable complexity through automated feedback loops.
3Productivity
If prediction accuracy is improved to optimize satellite resource allocation, then cost efficiency is improved, but measurement precision requirements increase
Solution Approach 1:
The system changes parameters by using multiple data sources (historical navigation data, movement graphs, current location) and adjusting prediction models based on vessel behavior patterns. This allows for improved prediction accuracy without requiring extremely precise measurement systems, as the approach leverages statistical patterns and probabilistic models rather than relying solely on high-precision real-time measurements.
Data Source
AI summary
A computer implemented method of tracking a travelling vessel, comprising obtaining a list of plurality of satellites capable of detecting the vessel at location(s) along predicted path(s) of the vessel. For each of the location(s) the following is performed:(a) Predicting vessel's possible future location(s) according to estimated movement vectors derived from a movement graph generated based on historical movement path(s), a recent movement path and a current location of the vessel.(b) Estimating satellites observation windows to identify candidate observation window(s) in which the satellite(s) have visual coverage of the possible future location(s).(c) Calculating detection score for each candidate observation window according to location probability score assigned to the possible future locations and view probability score assigned to the candidate observation windows.(d) Selecting preferred observation window presenting highest detection score.(e) Repeating (a)-(d) in case the vessel not detected in the selected observation window.