Sensor Data Fusion Using Entropy-Based Curation and Linking
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Solution Overview
Problem
Existing sensor data fusion systems fail to create actionable data by curating and linking sensor data before fusion, leading to excessive computational and storage requirements, and they do not generate new datasets that provide accuracy and predictive information.
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 fuse heterogeneous, partially heterogeneous, or homogeneous data points, creating a unique dataset with accuracy values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is fused without prior curation and linking, then the fusion process is simpler and faster, but computational and storage requirements become excessive and actionable data is not created
Solution Approach 1:
The system performs curation and linking of sensor data before the actual fusion process. The curation engine pre-processes raw sensor data by filtering, organizing, and validating it, while the link engine establishes relationships between different data points. This preliminary action reduces the volume and complexity of data entering the fusion stage, thereby decreasing computational and storage requirements during fusion while maintaining fusion speed.
Solution Approach 2:
The data fusion system is divided into distinct functional modules: a curation engine for pre-processing, a link engine for establishing relationships, and a fusion engine for combining data. This segmentation allows each component to handle specific tasks efficiently, preventing the need to process all raw data through the entire pipeline and reducing overall computational burden while maintaining productivity.
2Loss of information
If heterogeneous sensor data is fused directly, then all available data is utilized, but the system cannot generate new actionable datasets with accuracy values
Solution Approach 1:
The link engine acts as an intermediary between raw sensor data and the fusion process. It establishes mathematical relationships and connections between heterogeneous data points from different sensors, transforming raw data into linked data structures that preserve information while enabling the fusion engine to generate new actionable datasets with calculated accuracy values.
Solution Approach 2:
The system transforms sensor data by changing its parameter representation during curation and linking. Raw sensor readings are converted into standardized formats with associated metadata, confidence values, and relationship parameters. This parameter transformation enables the fusion engine to process heterogeneous data uniformly and generate new datasets with predictive accuracy information.
3Measurement precision
If comprehensive data curation and linking is performed before fusion, then new actionable data with accuracy values is created, but the system complexity increases
Solution Approach 1:
The curation engine and link engine are designed as universal components that can handle multiple types of sensor data through standardized interfaces and processing algorithms. These multi-functional engines can curate and link data from various sensor modalities (visual, auditory, tactile, etc.) using the same core mechanisms, reducing system complexity compared to having separate processing paths for each sensor type.
Solution Approach 2:
The system employs self-organizing algorithms in the curation and linking engines that automatically adapt to the characteristics of incoming sensor data without requiring manual configuration. The engines self-adjust their processing parameters and relationship models based on the data being processed, reducing the complexity of system setup and maintenance while maintaining high accuracy in the fused output.
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.