Sensor Data Fusion Through Curation and Conditional Entropy
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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 mathematically link and validate sensor data, creating a unique dataset that enhances accuracy and reduces computational and storage demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sources is fused to create actionable data, then data accuracy and predictive capabilities are improved, but computational and storage requirements increase
Solution Approach 1:
The system segments the data fusion process into distinct functional modules: a curation engine that filters and organizes raw sensor data, a link engine that identifies relationships between data points, and a fusion engine that combines correlated data. This segmentation allows each module to process only relevant portions of data independently, reducing the computational burden compared to fusing all sensor data simultaneously while maintaining fusion accuracy.
2Productivity
If heterogeneous sensor data sources are correlated and fused in real-time, then actionable data is created, but system complexity increases
Solution Approach 1:
The system employs a multi-functional processor architecture where a single processing unit dynamically performs multiple roles: data curation, correlation analysis, and data fusion. The processor adapts its operation based on the type of sensor data being processed and the specific fusion task required, eliminating the need for separate dedicated hardware for each function and thereby reducing overall system complexity while maintaining real-time fusion capability.
3Reliability
If mathematical validation is performed on fused sensor data, then data reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary data curation and correlation analysis before the actual fusion operation. The curation engine pre-processes sensor data by filtering out obviously invalid readings and organizing data by source and timestamp. The link engine pre-identifies correlated data relationships. These preliminary actions prepare the data in advance, so that when fusion and validation are required, the processor works with pre-organized, high-probability candidate data sets, significantly reducing validation time while maintaining reliability.
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.