Sensor Data Fusion with Pre-Curation for Real-Time Correlation
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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 power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data is stored and processed after collection, then data availability is improved, but computational and storage requirements increase excessively
Solution Approach 1:
The system performs data curation and mathematical linking of sensor data before storage occurs. The curation engine processes incoming sensor data to identify correlations and create mathematically linked datasets in advance, so that when data is stored, it is already organized and correlated rather than requiring post-collection processing of raw data
Solution Approach 2:
The patent divides the data processing function into separate engine components (curation engine, mathematical linking engine, fusion engine) that operate at different stages. This segmentation allows data to be processed incrementally through multiple specialized stages rather than requiring all processing to occur after complete data collection
2Adaptability or versatility
If heterogeneous sensor data sources are fused without pre-curation, then data fusion capability is improved, but computational processing requirements increase
Solution Approach 1:
The curation engine performs preliminary processing of heterogeneous sensor data by categorizing, filtering, and organizing data from multiple sources before the fusion process. This pre-curation reduces the complexity of data fusion operations by ensuring data is properly structured and correlated in advance
Solution Approach 2:
The mathematical linking engine acts as an intermediary between raw sensor data and the fusion process. It creates mathematical relationships and correlations between data points from different heterogeneous sources, transforming them into a unified format that facilitates efficient fusion while reducing computational complexity
3Loss of information
If all sensor data is retained and processed, then data completeness is improved, but power consumption increases
Solution Approach 1:
The curation engine extracts and retains only the most relevant and correlated data points from the complete sensor dataset. By identifying and removing redundant or less significant data through mathematical linking and correlation analysis, the system maintains data completeness for critical information while reducing the overall data volume that requires processing and storage
Solution Approach 2:
The system transforms sensor data parameters by creating mathematical relationships and correlations between data points. This parameter transformation consolidates information, allowing the system to represent complete data states more efficiently with fewer processed parameters, thereby reducing computational power requirements
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.