Sensor Data Time Alignment for Multi-Sensor Event Detection
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Solution Overview
Problem
Integration of data from different types of sensors in complex environments is challenging due to varying response times and responsiveness, leading to inaccurate correlation of sensor data and event detection.
Innovation Solution
A method for time alignment of sensor data involving determining time delays for each sensor, providing time correction factors, and using channelization with reference signals to align data, followed by threshold detection and event inference from multiple sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data from multiple sensor types are integrated without time correction, then the system can process data from all sensors, but the correlation accuracy between sensor data and events deteriorates due to varying response times
Solution Approach 1:
The patent applies preliminary action by determining time delays for each sensor in advance using reference signals before actual event detection. Time correction factors are calculated and stored beforehand, so that when sensor data is integrated during event detection, the corrections are already available, improving correlation accuracy without adding complexity to the real-time processing pipeline
Solution Approach 2:
The patent introduces time correction factors as an intermediary element between raw sensor data and event correlation. These correction factors act as mediators that compensate for varying sensor response times, allowing accurate event detection without requiring complex real-time synchronization mechanisms
2Measurement precision
If time delay calibration using reference signals is implemented, then time alignment accuracy is improved, but the setup time and initial configuration effort increase
Solution Approach 1:
The time delay calibration is performed as a preliminary action during system initialization or setup phase. Once the time correction factors are determined and stored, they can be reused for multiple event detection operations, so the initial time investment pays off through improved accuracy in all subsequent measurements
Solution Approach 2:
The patent changes the temporal parameter of sensor data by applying time corrections based on determined delay characteristics. This parameter transformation aligns all sensor readings to a common time reference, enabling accurate event correlation while the correction parameters are established once during setup
3Measurement precision
If computational resources are increased to process all sensor data in real-time, then event detection accuracy is improved, but energy consumption and computational burden increase
Solution Approach 1:
Complex computational tasks such as time delay determination and correction factor calculation are performed in advance during setup or idle periods. This preliminary processing reduces the computational burden during real-time event detection, as the system only needs to apply stored correction factors rather than perform complex calculations on every data point
Solution Approach 2:
The sensor system performs self-calibration by using reference signals to automatically determine its own time delay characteristics. This self-service approach eliminates the need for external calibration equipment or manual adjustment, reducing both computational overhead and operational complexity
Data Source
AI summary
A method for time alignment of sensor having a plurality of sensors for use in sensor data integration. A reference signal, with timestamp, is provided to each sensor. Output signals in response to the reference signal are identified and used to determine a time delay for response for the sensors. These time delays are stored applying time corrections to sensor data during sensor data integration used in event detection. An associated method of detecting events from sensor data from a plurality of sensors is disclosed. A threshold is determined for each of the sensors, such that a signal exceeding the threshold is identified as a potential sensor event. Potential sensor events from each sensor are detected and when potential events from at least two sensors fall within a predetermined time window this is identified to be a likely actual event.