Barometric Pressure Sensor Calibration via Sporadic Server Data
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Solution Overview
Problem
Consumer-grade barometric pressure sensors in mobile devices are often not well-calibrated, and user calibration is either unwilling or unreliable, leading to inaccurate altitude determination over time due to calibration drift.
Innovation Solution
A method where a server receives data packets from devices, determines calibration results, updates a historical calibration table based on satisfying specific rules regarding calibration offsets and confidence intervals, and selects a combined calibration value for transmitting back to the device for sensor calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automatic calibration system is implemented, then user intervention is reduced, but calibration accuracy becomes questionable when techniques are insufficiently adapted to user situations
Solution Approach 1:
The calibration system dynamically adapts its techniques based on detected user situations and environmental conditions. The server selects from multiple calibration techniques depending on whether the device is indoors or outdoors, and adjusts the approach based on user behavior patterns, making the system both automatic and accurate.
Solution Approach 2:
The system changes calibration parameters and techniques based on detected conditions. When the device is detected to be outdoors with good GPS signal, different calibration parameters are used compared to when it's indoors. The server modifies calibration approaches based on user situation parameters.
2Measurement precision
If frequent calibration data collection is performed, then calibration accuracy is improved, but power consumption increases
Solution Approach 1:
Instead of continuous calibration data collection, the system performs calibration sporadically based on triggered events such as location changes, altitude changes, or user-initiated requests. The server determines when calibration is actually needed and collects data only at those periodic intervals, reducing power consumption while maintaining accuracy.
Solution Approach 2:
The calibration system operates autonomously without requiring continuous user activation. The server monitors device usage patterns and automatically initiates calibration when appropriate conditions are met, eliminating the need for continuous power-intensive data collection while maintaining calibration accuracy.
3Reliability
If comprehensive calibration data is collected, then calibration reliability is improved, but privacy concerns increase
Solution Approach 1:
The system extracts and processes calibration data on the server side rather than storing comprehensive raw data on the user device. Only essential calibration parameters and results are retained, while detailed location and usage data are processed transiently and discarded, maintaining calibration reliability while minimizing privacy risks.
Solution Approach 2:
The server acts as an intermediary that processes calibration data without requiring the user device to store or transmit comprehensive raw data. The server receives minimal necessary data, performs calibration processing, and returns results, thereby maintaining reliability while reducing the privacy exposure of user data.
Data Source
AI summary
Multiple calibration results for calibrating a barometric pressure sensor based on data received from a device containing the sensor are determined and stored in a table. The table is updated based on rules regarding a relationship between each calibration result and a current calibration value. The calibration results are weighted and combined to determine a combined calibration result. The calibration value for calibrating the sensor is selected from the calibration results, the combined calibration results, or the current calibration value based on a selection criteria.