Sensor Data Fusion Using Linking and Validation Engines
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Solution Overview
Problem
Existing sensor data fusion systems fail to create actionable data by linking and fusing heterogeneous, partially heterogeneous, or homogeneous data sources in real-time, leading to excessive computational and storage requirements, and lack of accuracy in determining sensor reliability and predicting future events.
Innovation Solution
A system and method for sensor data fusion that includes a computer processor with curation, linking, fusion, inference, and validation engines to mathematically link and fuse sensor data, creating a unique dataset validated by artificial intelligence, reducing computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data from multiple sources is fused without mathematical linking and validation, then data processing speed is improved, but data accuracy and reliability determination deteriorate
Solution Approach 1:
The system performs preliminary mathematical linking and validation of sensor data relationships before fusion. The validation engine pre-establishes mathematical relationships between sensor outputs and validates data consistency, so that when fusion occurs, the data is already prepared and verified, maintaining both speed and accuracy
Solution Approach 2:
The validation engine acts as an intermediary between raw sensor data and the fusion process. It introduces mathematical validation as an intermediate step that verifies data relationships without significantly delaying the overall processing pipeline, thus maintaining productivity while improving measurement precision
2Device complexity
If heterogeneous sensor data is fused without curating and linking, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The system segments the data fusion process into distinct functional modules: curation engine for data preparation, linking engine for establishing relationships, validation engine for verification, and fusion engine for integration. This segmentation manages complexity by organizing functions separately while preserving all data relationship information through dedicated processing stages
Solution Approach 2:
The computer processor is designed with multi-functional engines that can handle various sensor types and data formats universally. The curation, linking, validation, and fusion engines work together to process heterogeneous data without requiring separate specialized systems, thus reducing overall device complexity while preventing information loss
3Reliability
If all sensor data is stored for future analysis, then data availability is improved, but storage requirements increase
Solution Approach 1:
The system extracts and stores only the essential mathematical relationships and validated fusion results rather than storing all raw sensor data. By taking out only the critical information needed for future analysis, the system maintains data availability while significantly reducing storage requirements
Solution Approach 2:
The system discards redundant raw sensor data after fusion while recovering and preserving the essential mathematical relationships and validation results. This allows future analysis to be performed on compressed, essential information rather than complete raw datasets, balancing availability and storage
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.