Sensor Data Synchronization via Cross-Correlation
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Solution Overview
Problem
Current telematics systems face challenges in accurately synchronizing data across multiple sensors due to unpredictable delays, leading to potential errors when associating time stamps from Global Navigation Satellite System (GNSS) sensors with data recorded by telematics systems.
Innovation Solution
A method and system that record sensor data with universal time stamps, correlate data from sensors with and without universal time stamps, and determine a universal time for data without a universal time stamp based on this correlation, using techniques like spline interpolation and cross-correlation to adjust time steps and align data sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If universal time stamps from GPS satellites are used for synchronizing sensor data, then time accuracy is improved, but system complexity and cost increase due to reliance on external GPS infrastructure and processing delays
Solution Approach 1:
The patent introduces a correlation mechanism as an intermediary that bridges sensor data with and without universal time stamps. By correlating data sequences and calculating time offsets, the system mediates between the need for accurate time synchronization and the reality of processing delays, eliminating the need for complex GPS infrastructure while maintaining time accuracy.
Solution Approach 2:
The patent creates a virtual copy of the universal time stamp for sensor data that originally lacked one. By calculating the universal time stamp based on correlated data from sensors that do have time stamps, the system generates a synthesized time reference that maintains synchronization accuracy without requiring direct GPS signal integration for all sensors.
2Reliability
If sensor data is recorded with universal time stamps, then data synchronization is improved, but data loss occurs when sensors cannot provide accurate time information
Solution Approach 1:
The patent implements a feedback mechanism where time offset information derived from correlated data is fed back to adjust the timing of sensor data. By continuously monitoring the correlation between sensors with and without time stamps, the system refines its time synchronization and compensates for information loss through iterative correction.
Solution Approach 2:
The patent performs preliminary correlation analysis between sensor data streams before final time stamp assignment. By pre-establishing the temporal relationship between sensors that have time stamps and those that don't, the system prepares time offset corrections in advance, ensuring that time information can be recovered even when individual sensors lack accurate timing data.
3Measurement precision
If data from sensors with and without universal time stamps is correlated, then synchronization accuracy is improved, but processing time increases due to additional correlation computations
Solution Approach 1:
The patent applies partial correlation by focusing the correlation computation only on the time offset calculation rather than requiring full data alignment. By performing correlation only where needed (between sensors with and without time stamps) and using only the necessary temporal information, the system achieves synchronization accuracy without the excessive processing time that would result from complete data correlation.
Data Source
AI summary
Various embodiments of the present invention provide methods, systems, apparatus, and computer program products for synchronizing data across at least two sensors. In one embodiment, first sensor data, which includes a universal time stamp from a universal time source, is recorded from at least one first sensor and second sensor data, which does not include a universal time stamp from a universal time source, is recorded from at least one second sensor. The first sensor data is correlated with the second sensor data, and a universal time that corresponds with the data from the second sensor is determined at least in part based on the correlation between the first sensor data and the second sensor data.