Sensor Data Fusion Using Validation and Correlation Filtering

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Solution Overview

Problem

Existing sensor data fusion systems fail to create actionable data by correlating and fusing heterogeneous, partially heterogeneous, or homogeneous data sources in real-time, leading to excessive computational and storage requirements, and lack of sensor accuracy assurance.

Innovation Solution

A system and method for sensor data fusion that includes a computer processor with curation, link, fusion, inference, and validation engines to curate and mathematically link sensor data before storage, creating a unique dataset with enhanced accuracy and reduced computational and storage demands.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If sensor data from multiple sources is fused without mathematical validation, then data completeness is improved, but data accuracy deteriorates

Engineering Contradiction:

Improvedata completenessVSAvoiddata accuracy

Core Design Contradiction:

Quantity of substanceVSMeasurement precision

Solution Approach 1:

The system performs preliminary mathematical validation and correlation analysis between sensor data sources before fusing the data. The validation engine checks mathematical relationships and correlations between data points from different sensors, ensuring data accuracy is maintained while achieving completeness through fusion of validated sources.

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If all sensor data is stored and processed, then data availability is improved, but computational requirements increase

Engineering Contradiction:

Improvedata availabilityVSAvoidcomputational requirements

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The system extracts and processes only the essential mathematical relationships and correlated data points from sensor sources. By identifying and retaining only the mathematically validated correlations rather than storing all raw sensor data, the system maintains data availability while significantly reducing computational and storage requirements.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system transforms raw sensor data into mathematically validated correlation parameters and relationships. By changing the data representation from raw sensor readings to validated mathematical correlations, the system reduces data volume and computational requirements while preserving essential information for decision-making.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If heterogeneous sensor data is fused without correlation analysis, then data integration is improved, but reliability deteriorates

Engineering Contradiction:

Improvedata integrationVSAvoidsensor accuracy assurance

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system performs preliminary correlation analysis and mathematical validation between heterogeneous sensor data sources before integrating them. The validation engine establishes mathematical relationships and confidence levels between different sensor types, ensuring that only reliably correlated data is fused, thereby maintaining both integration capability and accuracy assurance.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12576534B2Systems and methods of sensor data fusion

Publication Date: 2026.03.17 DIGITAL GLOBAL SYSTEMS INC

AI summary

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.