Real-Time Sensor Fusion with Pre-Correlation and Data Curation
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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 the ability to provide sensor accuracy and predict future events.
Innovation Solution
A system and method for sensor data fusion that includes a computer processor with curation, linking, fusion, inference, and validation engines to create a unique dataset by correlating and fusing sensor data in near real-time, reducing computational and storage demands while enhancing accuracy and predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sensor data from multiple sources is collected and stored for later analysis, then data completeness and information breadth are improved, but computational requirements and storage needs increase excessively
Solution Approach 1:
The system performs preliminary data fusion and correlation analysis in real-time as sensor data streams are received, rather than storing all raw data for later processing. The curation engine pre-processes incoming data by filtering, validating, and organizing it into structured formats, while the linking engine establishes relationships between data points before they are fused. This preliminary action reduces the volume of data requiring long-term storage while preserving the essential information needed for actionable insights and future analysis.
Solution Approach 2:
The fusion engine extracts only the essential and correlated features from multiple sensor data sources, creating a condensed representation that captures the most important information. Rather than storing complete raw datasets, the system extracts key parameters, patterns, and relationships that are then stored in a compressed format. This extraction process maintains information breadth while dramatically reducing storage requirements.
2Speed
If heterogeneous sensor data is fused without pre-correlation, then processing speed is improved, but data accuracy and reliability deteriorate
Solution Approach 1:
The linking engine performs preliminary correlation analysis between heterogeneous sensor data sources before fusion, establishing relationships and validating data consistency in advance. This pre-correlation ensures that only compatible and mutually reinforcing data points are fused together, maintaining high accuracy while enabling efficient real-time processing. The validation engine further reinforces this by checking data quality metrics before fusion occurs.
Solution Approach 2:
The curation engine acts as an intermediary layer between raw sensor inputs and the fusion process, standardizing and validating data from diverse sources before they enter the fusion pipeline. This intermediary processing ensures that heterogeneous data are properly formatted, synchronized, and verified for consistency, enabling accurate fusion without sacrificing processing speed.
3Reliability
If all sensor data is processed and stored for future reference, then data availability for predictions is improved, but computational complexity and energy consumption increase
Solution Approach 1:
The system extracts and stores only the essential correlated features and patterns from sensor data that are most relevant for predictive analysis. Rather than maintaining complete historical datasets, the extraction process identifies and preserves key parameters, trends, and relationships that drive prediction accuracy. This selective storage significantly reduces energy requirements for data management while maintaining reliable predictive capabilities.
Solution Approach 2:
The fusion engine transforms raw sensor data into standardized parameters and features with consistent formats and units, enabling efficient storage and processing. By changing the parameter representation from raw heterogeneous data to normalized feature vectors, the system reduces computational complexity for future predictions while preserving the essential information needed for accurate modeling and forecasting.
4Productivity
If real-time fusion of heterogeneous data is performed, then actionable data creation is improved, but device complexity increases
Solution Approach 1:
The system segments the complex data fusion process into distinct functional modules: a curation engine for data preparation, a linking engine for relationship establishment, a fusion engine for data integration, and a validation engine for quality assurance. Each module handles specific tasks independently, reducing overall system complexity while enabling real-time processing of heterogeneous data. This modular architecture allows each component to be optimized separately and facilitates easier maintenance and scaling.
Solution Approach 2:
The fusion engine is designed as a universal processor that can handle multiple types of sensor data (visual, audio, tactile, etc.) through standardized interfaces and algorithms. Rather than requiring separate processing pipelines for each sensor type, the multi-functional fusion engine applies consistent correlation and integration methods across diverse data sources, reducing system complexity while maintaining the ability to create actionable insights from heterogeneous inputs.
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.