Sensor Data Fusion with Curation and Validation Engines
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Solution Overview
Problem
Existing sensor data fusion systems face challenges in accurately fusing heterogeneous, partially heterogeneous, or homogeneous data sources, leading to inefficiencies in computational processing, storage, and scalability, while also failing to provide actionable data on sensor accuracy and predictive capabilities.
Innovation Solution
A system and method for sensor data fusion that utilizes a computer processor with multiple engines (curation, link, fusion, inference, and validation) to curate, link, and fuse sensor data from diverse sources, creating new data points and validating their accuracy, thereby enhancing sensor accuracy and predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data from multiple heterogeneous sources is fused using traditional methods, then data integration is achieved, but computational processing requirements and storage demands increase significantly
Solution Approach 1:
The patent segments the sensor data fusion process into distinct functional modules: a curation engine that pre-processes and categorizes data from different sources, a link engine that establishes relationships between curated data points, and a fusion engine that performs the actual fusion operation. This segmentation allows each module to handle specific tasks independently, reducing the computational burden on any single component and enabling scalable architecture.
Solution Approach 2:
The curation engine performs preliminary actions by pre-processing, categorizing, and filtering sensor data before it reaches the fusion engine. This preliminary curation reduces the volume and complexity of data that requires intensive processing during fusion, thereby decreasing computational requirements and storage demands while maintaining integration capability.
2Adaptability or versatility
If traditional sensor data fusion methods are used, then data integration is achieved, but scalability is limited
Solution Approach 1:
The patent creates a universal sensor data fusion system where the curation engine, link engine, and fusion engine can handle multiple types of sensor data from diverse sources through a common architecture. The system's modular design allows it to scale by adding new sensor types or data sources without requiring fundamental changes to the core fusion mechanism, thereby improving scalability while maintaining integration capability.
3Loss of information
If sensor data fusion creates new data points, then predictive capabilities are enhanced, but validation of data accuracy becomes more challenging
Solution Approach 1:
The patent incorporates a validation engine that provides feedback on the accuracy and reliability of fused data points. This validation mechanism assesses the quality of new data points generated during fusion, verifying their accuracy against established criteria and providing feedback that can be used to refine the fusion process, thereby maintaining measurement precision while enhancing predictive capabilities.
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.