Smartphone Barometer Calibration Using Crowd Pressure Data
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Solution Overview
Problem
Conventional methods for determining altitude using smartphone barometers are inaccurate due to barometer bias, weather conditions, and imprecise ground level data, requiring complex hardware calibration or being grossly inaccurate.
Innovation Solution
Collect and analyze location and atmospheric pressure data from multiple client devices over time to estimate and calibrate the barometer bias, using the law of large numbers to achieve precise altitude determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to determine altitude using smartphone barometers, then the process is simple and requires no calibration, but the altitude determination is grossly inaccurate
Solution Approach 1:
The system performs self-calibration by automatically collecting atmospheric pressure data from multiple devices, identifying ground level locations, and computing device-specific bias corrections without requiring manual user intervention or complex hardware calibration procedures
Solution Approach 2:
The server acts as an intermediary that collects data from multiple client devices, processes the information to determine ground level and barometer bias, and returns calibration parameters to individual devices, thereby distributing the calibration complexity across the network rather than requiring it at each device
2Measurement precision
If complex hardware calibration is performed to improve altitude accuracy, then measurement precision improves, but the ease of operation deteriorates
Solution Approach 1:
The calibration process occurs automatically in the background without requiring user action. The system collects atmospheric pressure readings, determines ground level locations, and applies bias corrections autonomously, making the complex calibration transparent to the end user
Solution Approach 2:
The system performs preliminary calibration by collecting atmospheric pressure data and determining device bias before the user needs accurate altitude information, so that when altitude measurement is required, the calibration is already complete and no user action is needed
3Measurement precision
If ground level data is used to determine altitude, then the process is straightforward, but the data is imprecise leading to inaccurate results
Solution Approach 1:
The system uses feedback from multiple atmospheric pressure measurements at suspected ground level locations to iteratively refine the ground level determination and device bias correction, improving data quality through repeated measurement and validation
Solution Approach 2:
The system combines atmospheric pressure data from multiple client devices at the same location to determine ground level and device bias, merging multiple data sources to overcome the imprecision of individual measurements and create more reliable calibration data
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Accurately determines user altitude by calibrating the barometer bias, providing stable long-term results through averaging large datasets, and sharing floor level information with approved contacts via a map GUI.
Implementation Method 1
Since air pressure decreases with altitude according to a physics formula, it is theoretically possible to determine an altitude of a client device from atmospheric pressure readings
Data Source
AI summary
Methods, systems, and devices for calibrating a barometer of a client device. A server computer accesses historical data including location data, and atmospheric pressure data collected from a plurality of client devices over a period of time. An equation system defined by the historical data is solved. The equation system has a plurality of unknown parameters, the plurality of unknown parameters comprising a barometer bias of a first client device among the plurality of client devices. The first client device is calibrated using the barometer bias.