Sensor Data Fusion Using Curation and Conditional Entropy
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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 inefficiencies in data processing, storage, and computational requirements, leading to reduced accuracy and increased power consumption.
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 demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data from multiple sources is fused using traditional methods, then more comprehensive data is obtained, but computational requirements and power consumption increase significantly
Solution Approach 1:
The patent segments the data fusion process into distinct functional modules: a curation engine that pre-processes and organizes sensor data, a link engine that establishes relationships between data points, and a fusion engine that performs the actual fusion. This segmentation allows each module to operate efficiently on specific data types and reduces overall computational overhead compared to monolithic fusion approaches.
Solution Approach 2:
The curation engine performs preliminary actions by pre-processing, filtering, and organizing sensor data before it reaches the fusion engine. This includes validating data quality, standardizing formats, and pre-establishing data relationships, which reduces the computational burden during actual fusion operations and lowers real-time power consumption.
2Measurement precision
If heterogeneous sensor data is fused without preprocessing, then data processing speed is maintained, but fusion accuracy decreases due to data inconsistencies
Solution Approach 1:
The curation engine acts as an intermediary between raw sensor data and the fusion process. It standardizes heterogeneous data formats, validates data quality metrics, and pre-establishes relationships between different sensor types, thereby improving fusion accuracy without requiring complex processing logic in the fusion engine itself.
Solution Approach 2:
The patent replaces complex mechanical data processing operations with algorithmic solutions. The link engine uses automated algorithms to establish relationships between data points based on spatial, temporal, and semantic criteria, eliminating the need for manual data alignment and reducing processing complexity while maintaining high accuracy.
3Loss of information
If all sensor data is stored for future analysis, then data completeness is improved, but storage requirements increase significantly
Solution Approach 1:
The system extracts only the essential and relevant features from raw sensor data during the curation phase, storing these extracted features rather than complete raw datasets. This extraction process maintains data completeness for analysis purposes while significantly reducing storage requirements by eliminating redundant information.
Solution Approach 2:
The curation engine discards redundant, duplicate, or low-quality sensor data points that do not contribute to fusion accuracy, while recovering and preserving critical data elements needed for future analysis. This selective discarding and recovering approach maintains information completeness for essential parameters while reducing overall storage requirements.
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.