Sensor Data Fusion Curation for Accurate Low-Load Processing
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Solution Overview
Problem
Existing sensor data fusion systems fail to accurately fuse heterogeneous, partially heterogeneous, or homogeneous data sources due to inefficient data processing and storage requirements, leading to reduced computational efficiency and increased storage demands.
Innovation Solution
A system and method for sensor data fusion that utilizes a computer processor with a curation engine, link engine, fusion engine, inference engine, and validation engine to curate, link, fuse, infer, and validate sensor data from multiple sources, creating a unique dataset with enhanced accuracy and reduced computational and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data from multiple sources is fused to improve accuracy, then measurement precision is improved, but computational processing requirements increase
Solution Approach 1:
The patent segments the sensor data fusion process into distinct functional modules: a curation engine that pre-processes and organizes raw sensor data, a link engine that establishes relationships between curated data elements, and a fusion engine that performs the actual data fusion. This segmentation allows each module to operate independently and efficiently, reducing the overall computational burden while maintaining fusion accuracy.
Solution Approach 2:
The patent applies preliminary action by implementing a curation engine that pre-processes sensor data before fusion operations. The curation engine filters, validates, and organizes raw sensor data, eliminating redundant and invalid data points in advance. This preliminary curation reduces the volume of data requiring intensive fusion computations, thereby lowering processing requirements while preserving measurement precision.
2Measurement precision
If sensor data from multiple sources is fused to improve accuracy, then measurement precision is improved, but storage demands increase
Solution Approach 1:
The patent extracts only the essential and relevant features from raw sensor data during the curation phase. The curation engine identifies and retains valid data elements while discarding redundant information, and the link engine extracts key relationships between data points. This extraction process reduces the volume of data that must be stored for fusion operations, decreasing storage demands while maintaining the precision needed for accurate fusion results.
Solution Approach 2:
The patent applies local quality by curating sensor data according to its specific characteristics and validity criteria. Different data sources are curated with tailored filters and validation rules appropriate to their nature, ensuring that only high-quality, relevant data is retained for fusion. This selective retention of locally optimized data quality reduces overall storage requirements while preserving the precision needed for accurate fusion outcomes.
3Measurement precision
If heterogeneous sensor data is processed to improve fusion accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements universality through a standardized data curation framework that handles multiple heterogeneous sensor data types through a common interface. The curation engine applies universal validation and filtering rules that work across different sensor modalities, while the link engine uses a unified approach to establish relationships between diverse data elements. This universal processing framework reduces system complexity by avoiding the need for separate specialized processing paths for each sensor type, while still achieving high fusion accuracy.
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.