Sensor Data Fusion with Curation and Linking 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 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 link sensor data before storage, creating a unique dataset with mathematical associations and reduced power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If sensor data is fused in real-time without pre-curation and linking, then data fusion speed is improved, but computational and storage requirements increase excessively
Solution Approach 1:
The system performs preliminary curation and linking of sensor data before the actual fusion process. The curation engine pre-processes sensor data by categorizing it into properties and sub-properties, while the link engine pre-establishes mathematical associations between curated data points. This preliminary processing reduces the computational burden during real-time fusion operations.
Solution Approach 2:
The data fusion system is divided into distinct functional modules: a curation engine for data categorization, a link engine for establishing associations, and a fusion engine for combining data. This segmentation allows each component to specialize in specific tasks, improving overall efficiency and reducing redundant computations during real-time operation.
2Device complexity
If heterogeneous sensor data is fused without mathematical validation, then data fusion complexity is reduced, but sensor accuracy assurance deteriorates
Solution Approach 1:
The validation engine continuously monitors and validates the mathematical associations between fused sensor data points. It verifies that the relationships established by the link engine maintain expected correlations and accuracy thresholds, providing feedback to ensure data quality without requiring complex manual validation procedures.
Solution Approach 2:
The system dynamically adjusts validation thresholds and mathematical association parameters based on the specific sensor types and data characteristics being fused. This allows the system to maintain appropriate accuracy standards for different sensor combinations without using a single overly complex validation framework.
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.