Sensor Data Correlation and Validation for Real-Time Fusion
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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
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sources is fused without mathematical validation, then data fusion can be performed quickly, but the accuracy and reliability of the fused data cannot be assured
Solution Approach 1:
The system performs preliminary mathematical validation and correlation analysis on sensor data before fusion. The validation engine checks mathematical relationships between sensor outputs and validates correlations exceed thresholds before data is fused, ensuring accuracy is assured in advance rather than after fusion.
Solution Approach 2:
A validation engine acts as an intermediary between data fusion and the rest of the system. This intermediary component validates sensor accuracy and mathematical correlations before data proceeds to fusion, separating the validation function from the fusion process itself.
2Loss of information
If all sensor data is stored before processing, then complete data is available for analysis, but storage requirements become excessive
Solution Approach 1:
The system extracts only the essential mathematical relationships and validated correlations from sensor data, storing only these extracted features rather than the complete raw sensor datasets. This extraction approach maintains the necessary information for analysis while dramatically reducing storage requirements.
Solution Approach 2:
The system performs preliminary validation and correlation analysis before storage, extracting and storing only the validated mathematical relationships and correlation metrics. This preliminary processing ensures that only essential, validated information is stored, maintaining data completeness while minimizing storage needs.
3Reliability
If heterogeneous sensor data is fused without mathematical validation, then data fusion can be performed, but the reliability of the fused dataset is compromised
Solution Approach 1:
The validation engine performs preliminary mathematical validation and correlation verification on heterogeneous sensor data before fusion. By validating data relationships in advance and only fusing data that meets validation thresholds, the system ensures reliability is established before processing, preventing unreliable data from proceeding to fusion.
4Measurement precision
If sensor data is mathematically validated and correlated before fusion, then data accuracy is improved, but computational processing time increases
Solution Approach 1:
The system extracts and stores only the essential mathematical validation results and correlation metrics from sensor data. By extracting only the critical validation information rather than performing complete validation during each fusion operation, the system maintains high data accuracy while reducing the computational time required for subsequent processing.
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.