Sensor Data Fusion Using Curated Links for Real-Time Accuracy
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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 the ability to generate new datasets that enhance sensor accuracy and predict future events.
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 that enhances accuracy and reduces computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data from multiple sources is fused without mathematical validation, then data processing speed is improved, but data accuracy and reliability deteriorate
Solution Approach 1:
The system performs mathematical validation and correlation analysis on sensor data before fusing it with other data sources. This preliminary validation ensures that only accurate and reliable data is incorporated into the fused dataset, maintaining data quality while enabling efficient processing through pre-established mathematical relationships.
2Adaptability or versatility
If heterogeneous sensor data is stored before fusion, then data availability is improved, but storage requirements and computational demands increase
Solution Approach 1:
The system extracts and stores only the essential mathematical relationships and correlation coefficients between sensor data sources, rather than storing complete raw datasets. This extraction approach maintains data availability for fusion operations while dramatically reducing storage requirements and computational overhead.
3Speed
If real-time sensor data fusion is performed without pre-established mathematical links, then response time is improved, but system complexity increases
Solution Approach 1:
The system pre-establishes mathematical validation rules and correlation models between sensor data sources during system initialization or data setup phases. These pre-configured mathematical links enable real-time fusion operations to proceed efficiently without complex runtime calculations, reducing both response time and operational complexity.
Data Source
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.