Sensor Timestamping via Sampling Rate Estimation
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Solution Overview
Problem
Existing sensor systems face inaccuracies in timestamp generation, particularly when the processor is not timely in applying timestamps to sensor samples, leading to potential errors in determining the orientation of electronic devices like smartphones and GPS units.
Innovation Solution
A processor-implemented method that estimates the sampling rate of sensors, determines an expected sample time, detects sensor samples, assigns a reporting time, and calculates a timestamp based on both the expected and reporting times to improve timestamp accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the processor applies timestamps to sensor samples, then timestamp information is available for orientation determination, but the timestamp accuracy deteriorates due to processor delays
Solution Approach 1:
The sensor generates intermediate data structures containing timestamp information at the moment of sampling, before the processor is needed for other tasks. This preliminary action ensures that when the processor does handle the data, the timestamp is already prepared and associated with the sensor sample, eliminating delays in timestamp generation.
Solution Approach 2:
An intermediate data structure is introduced between the sensor and the processor. This data structure serves as a mediator that holds both the sensor sample and its associated timestamp information, allowing the sensor to prepare timestamp data independently while the processor can retrieve it without delay when needed for orientation calculations.
2Adaptability or versatility
If the processor handles multiple functions simultaneously, then device functionality is enhanced, but timestamp generation timeliness deteriorates
Solution Approach 1:
The system is segmented into independent functional components: the sensor handles sampling and intermediate data structure generation with timestamp information, while the processor handles orientation determination and other functions. This segmentation allows the sensor to operate independently and prepare timestamp data without being blocked by the processor's multiple tasks.
Solution Approach 2:
The sensor performs preliminary actions by generating intermediate data structures with timestamp information before the processor needs this data. This allows the processor to maintain its multiple functions while still receiving timely timestamp data, as the preparation work was done in advance by the sensor.
3Adaptability or versatility
If sampling rate variations occur, then sensor adaptability is improved, but timestamp accuracy deteriorates
Solution Approach 1:
The system dynamically adapts to varying sampling rates by having the sensor continuously update its intermediate data structures with current timestamp information. Each sensor sample carries its own dynamically generated timestamp, allowing the system to handle variable sampling rates while maintaining accurate timing information for each individual sample.
Solution Approach 2:
The sensor provides feedback about actual sampling times through the intermediate data structures, allowing the system to track and compensate for sampling rate variations. This feedback mechanism ensures that even when sampling rates change, each sample is associated with its precise acquisition time, maintaining timestamp accuracy.
Data Source
AI summary
Methods and devices for determining a timestamp that represents a time a sensor sample was generated are described. In one aspect, a method includes: obtaining a sampling rate estimate for the sensor; determining an expected sample time based on the sampling rate estimate; detecting a sensor sample and assigning a reporting time to the detected sensor sample, the reporting time representing the time when the sensor sample was detected; and determining the timestamp for the sensor sample based on the expected sample time and the reporting time.