Real-Time Sensor Data Fusion With Curation and Validation
Find Innovative SolutionsGenerate Solutions
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, creating a unique dataset with enhanced accuracy and reduced computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data from multiple sources is collected and stored for later analysis, then data availability and analysis completeness are improved, but computational requirements and storage needs increase excessively
Solution Approach 1:
The system performs preliminary data fusion and correlation analysis at the point of data generation rather than storing all raw data for later processing. The curation engine pre-processes incoming sensor data by filtering, categorizing, and correlating data points in real-time, creating a condensed representation that preserves essential information while dramatically reducing storage requirements.
Solution Approach 2:
The system extracts only the most relevant and actionable features from raw sensor data through the curation engine, which identifies and isolates key data characteristics. This extraction process removes redundant information while retaining the essential data needed for accurate environmental representation and analysis.
2Productivity
If heterogeneous sensor data is fused without mathematical validation, then processing speed is improved, but data accuracy and reliability deteriorate
Solution Approach 1:
The validation engine implements continuous feedback loops that monitor and verify the accuracy of fused data points. Mathematical validation rules and correlation thresholds are applied dynamically, with the system adjusting processing parameters based on validation results to maintain both speed and accuracy.
Solution Approach 2:
The system dynamically adjusts validation stringency and processing depth based on data characteristics and operational context. The curation engine modifies correlation thresholds and validation requirements in real-time, allowing faster processing for high-confidence data while applying more rigorous validation when uncertainty is detected.
3Loss of information
If all sensor data is processed and stored with equal detail, then data completeness is improved, but computational complexity and power consumption increase
Solution Approach 1:
The system applies different levels of processing detail to different data based on their importance and characteristics. The curation engine identifies high-value data points requiring detailed processing while applying lighter processing to less critical data, optimizing the balance between completeness and computational resource usage.
Solution Approach 2:
The system performs partial processing on most data points and excessive (detailed) processing only on critical data points identified by the curation engine. This selective approach ensures data completeness for essential parameters while reducing overall computational load and power consumption.
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.