Data Fusion

What is Data Fusion?

Data fusion is the process of integrating data from multiple sources to create a unified, accurate, and comprehensive view. In the maritime industry, where data can often be noisy or fragmented, fusion eliminates inconsistencies and enables advanced AI models to deliver full visibility and actionable insights. Without data fusion, organizations face challenges, like incomplete or unreliable information, which weakens their operational foundations.

How Does Data Fusion Work? 

Here’s how data fusion happens: 

1. Data acquisition and validation: data is continuously collected from various sources and standardized for analysis. Before moving forward, it is validated to remove inaccuracies, and redundant or erroneous information.
2. Feasibility and assignment: the validated data is evaluated to ensure it reflects realistic behavior patterns, after which it is assigned to the most appropriate entities, using advanced matching techniques.
3. Conflict resolution and refinement: in cases of conflicting data assignments, a scoring mechanism resolves any ambiguities. The data is then further refined to maintain the accuracy and integrity of entity paths.
4. Data optimization: the final step involves streamlining the dataset, retaining only the most relevant information to ensure efficient processing in downstream applications.

Why is Data Fusion a Must in the Maritime Industry?

The maritime industry faces immense challenges with the quality and reliability of data generated by automatic identification system (AIS) transmissions. These transmissions produce vast amounts of raw data, much of which is noisy, corrupted, or deliberately manipulated. 

Without a robust system to address these issues, organizations are left with an incomplete or misleading view of maritime activity, undermining their ability to track vessels accurately, or identify deceptive shipping practices (DSPs).

Data fusion is indispensable because it leverages big data technologies, distributed computing, and AI to process and analyze millions of data points in real-time. It tackles critical obstacles in three key ways:

1. Data noise: sophisticated filtering mechanisms ensure only valid and relevant data is processed, cutting through irrelevant or erroneous signals.
2. Data corruption: advanced validation techniques detect and fix anomalies, such as corrupted timestamps, or incorrect coordinates, preserving data integrity.
3. AIS manipulation: fusion cross-references multiple data sources and uses advanced algorithms to detect and counter manipulation attempts, such as GPS jamming or GNSS spoofing.

In an era of increasing technological threats and complex maritime operations, data fusion is essential for achieving accurate, reliable, and actionable insights to support safety, compliance, and operational efficiency.

What Are the Three Achievable Goals of Windward’s Fusion Process, and How Do They Benefit Customers?

1. Accurately define noteworthy vessel entities: a primary goal of Windward’s data fusion process is to create an accurate and complete representation of vessel activity by merging dynamic data (real-time movements) with static data (vessel characteristics such as name, IMO number, and attributes). This ensures that each vessel’s identity is correctly maintained, enabling reliable maritime analysis and decision-making.  

2. Historical accuracy: Windward’s data fusion process leverages historical data dating back to 2013, allowing for a long-term perspective on vessel movements and trends. This historical accuracy supports robust anomaly detection and provides deeper insights into maritime patterns over time, enhancing the reliability of the Maritime AI™ platform.  

3. Comprehensive coverage: with the vast and growing fleet of vessels transmitting AIS signals, Windward’s data fusion process aims to cover the majority of global maritime traffic. This comprehensive coverage ensures a complete, uninterrupted view of global maritime activity, enabling customers to navigate the complexities of the maritime landscape with confidence.  

Through these goals, Windward delivers unparalleled accuracy, historical insight, and global coverage, empowering customers to make data-driven decisions with clarity and precision.

What Are Three Benefits of Windward’s Data Fusion Process?

1. Enhanced data integrity: Windward’s data fusion process ensures reliable and accurate vessel tracking by systematically cleansing and validating AIS data. By minimizing errors, such as incorrect vessel identification or missed detections, it provides a solid foundation for confident maritime decision-making.
   
2. Advanced logic capabilities: the data fusion process is dynamic and continuously evolves to incorporate new information. This includes revisiting past decisions to refine vessel records when anomalies or updated data are detected. Such adaptability ensures the accuracy and relevance of maritime insights.
   
3. Proactive detection with the trigger algorithm: Windward’s data fusion includes an AI-driven trigger algorithm that analyzes vessel behavior to predict ownership changes. By identifying subtle shifts in patterns or operational areas, this proactive approach gives users early warnings and actionable insights for risk management.

With these benefits, Windward’s data fusion process empowers organizations to navigate maritime challenges with precision and foresight.

Use Case: How Data Fusion Exposes Identity Spoofing in Maritime Operations

Challenge 

Some vessels attempt to evade detection or scrutiny by frequently changing their identities – altering names, IMO numbers, or other identifying features. These tactics are often used to engage in deceptive shipping practices (DSPs), such as circumventing sanctions or concealing illegal activities.

Solution with Data Fusion 

Windward’s patented data fusion process integrates information from diverse sources, including satellite and terrestrial data, unstructured open-source intelligence, and AIS transmissions. By cross-referencing this data and applying advanced algorithms, Windward can trace a vessel back to its original identity, even when multiple attempts at identity spoofing are made.

Outcome 

With accurate, fused, and clean data, organizations gain actionable insights that enable them to take decisive action. Exposing identity spoofing helps minimize risk, ensure compliance with global regulations, and maintain the integrity of global trade operations.

Without true data fusion, achieving this level of visibility is impossible. But it’s important to recognize that not all fusion processes are equal – what some call “fusion” may simply be data cleaning. The difference lies in the depth of integration, validation, and the actionable insights that follow.

Want to hear more about Windward’s approach to data fusion? Feel free to contact us.