Sensor Data Fusion with Curation for Real-Time Actionable Data
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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
1Quantity of substance
If sensor data from multiple sources is collected and stored without fusion, then data completeness is maintained, but computational and storage requirements become excessive
Solution Approach 1:
The system performs preliminary data fusion and correlation analysis before storing sensor data. The curation engine processes and links data from multiple sensors in advance, creating a condensed representative dataset that preserves actionable information while reducing storage requirements. This preliminary processing eliminates the need to store redundant raw data from all sensors simultaneously.
Solution Approach 2:
The system merges data from heterogeneous sensor sources by identifying correlations and fusing related data points. The link engine combines information from multiple sensors that measure related parameters, creating integrated data representations that maintain completeness while reducing the total quantity of stored data through intelligent consolidation.
2Use of energy by moving object
If sensor data is processed in real-time without pre-curation, then data freshness is maintained, but computational power consumption increases
Solution Approach 1:
The curation engine performs preliminary data processing, filtering, and correlation analysis before real-time processing requirements arise. By pre-curating sensor data and establishing correlations in advance, the system reduces the computational burden during real-time operation, thereby lowering power consumption while maintaining data processing speed through optimized real-time operations on already-processed data.
3Measurement precision
If heterogeneous sensor data is fused without mathematical validation, then data integration is simplified, but measurement accuracy decreases
Solution Approach 1:
The link engine acts as an intermediary that establishes mathematical relationships and validation rules between heterogeneous sensor data sources. It creates correlation models that define how different sensor measurements relate to each other, providing a structured framework for accurate data fusion. This intermediary layer manages the complexity of validating heterogeneous data while ensuring measurement precision through mathematical rigor.
Solution Approach 2:
The system transforms heterogeneous sensor data into a unified parameter space through mathematical validation. By converting diverse sensor measurements into comparable, validated parameters with established relationships, the system maintains measurement precision across different sensor types while managing fusion complexity through standardized parameter transformations.
4Reliability
If all sensor data is stored for later analysis, then data availability is maximized, but storage requirements become excessive
Solution Approach 1:
The system performs preliminary fusion and correlation analysis to create a condensed dataset that preserves data availability for future analysis. By processing and linking sensor data in advance, the system stores only the essential correlated information needed for reliable analysis, maintaining data availability while significantly reducing storage capacity requirements through intelligent data consolidation.
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.