Dead Reckoning Maritime Data Fabrication Detection

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Solution Overview

Problem

Current methods for maritime vessel tracking, particularly using Automatic Identification System (AIS) signals, face challenges in detecting fabricated location-reporting signals, which can obscure illicit activities such as sanctions evasion, drug trafficking, and other criminal behaviors, making it difficult for organizations to identify safe vessels and comply with regulations without incurring unnecessary risks or costs.

Innovation Solution

A method that involves obtaining location-reporting signals, determining initial vessel location, speed, and heading, computing estimated locations, and comparing these with reported locations to identify potential fabrication, using AI classifiers and dead reckoning computations to predict true vessel paths and detect anomalies, thereby identifying fabricated signals and determining fraudulent periods.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If automated detection methods are implemented to identify fabricated location-reporting signals, then detection capability and reliability are improved, but device complexity and computational requirements increase

Engineering Contradiction:

Improvedetection capabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary dead reckoning computations to establish expected vessel positions and trajectories before comparing them with actual AIS location reports. By pre-calculating predicted locations based on historical speed and heading data, the system creates a baseline for anomaly detection, enabling automated identification of fabricated signals without requiring complex real-time analysis infrastructure

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual detection methods with automated computational algorithms that use dead reckoning mathematics and AI classifiers. This substitution of mechanical/manual processes with automated computational systems improves detection reliability while managing complexity through algorithmic standardization rather than human judgment

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Loss of time

If continuous monitoring and analysis of location-reporting signals is performed, then detection timeliness is improved, but resource consumption increases

Engineering Contradiction:

Improvedetection timelinessVSAvoidresource consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The system implements event-driven analysis that skips continuous processing and only performs detailed dead reckoning computations when anomalies are detected or at scheduled intervals. By rushing through analysis only when necessary rather than continuously, the system maintains timely detection capability while reducing overall computational resource consumption during normal operation

Inventive Principle:

Principle #21Skipping (Rushing through)

Solution Approach 2:

The monitoring system performs analysis at periodic intervals rather than continuously, comparing location reports at scheduled times to reduce computational load. This periodic execution of dead reckoning calculations and AI classification maintains detection timeliness within acceptable thresholds while significantly reducing resource consumption compared to continuous monitoring

Inventive Principle:

Principle #19Periodic action

3Measurement precision

If manual verification methods are used to confirm fabricated signals, then measurement precision is improved, but productivity and response time deteriorate

Engineering Contradiction:

Improveverification accuracyVSAvoidresponse time

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system implements self-service verification through automated dead reckoning computations and AI classifiers that independently analyze location reports without requiring manual intervention. The algorithm uses the vessel's own historical speed and heading data to generate predicted positions, then automatically compares these with actual reports to identify fabrications, maintaining high precision while enabling rapid automated response

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback loops where AI classifiers evaluate dead reckoning results and adjust detection thresholds based on patterns learned from multiple vessels and time periods. This feedback mechanism maintains high verification accuracy by continuously refining detection criteria while enabling automated decision-making that improves response time compared to static manual verification processes

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS12111405B2Dead reckoning-based analysis of fabricated maritime data

Publication Date: 2024.10.08 WINDWARD

AI summary

A method, system and product for dead reckoning-based analysis of fabricated maritime data. The method comprises determining an initial location of the maritime vessel; determining speed and heading information using the location-reporting signals; computing estimated location of the maritime vessel at a specific time, based on the initial location and based on the speed and heading information; comparing the estimated location with a reported location at the time according to location-reporting signals; based on the comparison, determining that the location-reporting signals are at least partially fabricated; and in response to the determination that the location-reporting signals are at least partially fabricated, performing a responsive action.