Sensor Data Fusion with Pre-Curated Linking for Real-Time Accuracy
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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 is stored and processed without pre-fusion, then data completeness is maintained, but computational requirements and storage needs become excessive
Solution Approach 1:
The system performs preliminary data fusion and mathematical linking of sensor data before storage, creating a curated dataset that reduces computational requirements for subsequent processing while maintaining data accuracy. The curation engine pre-processes sensor outputs by establishing mathematical relationships and fusing data from multiple sensors ahead of time.
Solution Approach 2:
The patent divides the data processing workflow into distinct functional engines: a curation engine that pre-processes and links sensor data mathematically, and a fusion engine that performs final data fusion. This segmentation allows computational work to be distributed and optimized across different processing stages.
2Loss of information
If heterogeneous sensor data is stored in raw form, then data availability is maximized, but storage requirements increase significantly
Solution Approach 1:
The curation engine performs preliminary fusion of heterogeneous sensor data by establishing mathematical links between different sensor outputs before storage. This pre-processing creates a compact curated dataset that maintains data availability for analysis while significantly reducing the storage space required compared to storing all raw sensor data.
3Productivity
If sensor data fusion is performed without mathematical validation, then processing speed increases, but data accuracy and reliability decrease
Solution Approach 1:
The system incorporates validation engines that provide feedback on the accuracy and reliability of fused sensor data. The validation engine assesses whether mathematical links between sensor data meet predefined thresholds, ensuring data quality while maintaining processing efficiency through automated validation routines.
4Loss of time
If real-time sensor data fusion is implemented, then actionable data is generated promptly, but computational complexity increases
Solution Approach 1:
The curation engine performs preliminary mathematical linking and fusion of sensor data in real-time before the data is stored or further processed. This pre-processing approach generates actionable data promptly while managing computational complexity by establishing mathematical relationships ahead of time rather than computing them on-demand during analysis.
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.