Mobile Pressure Sensor Calibration for Floor-Level Altitude Estimation
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Solution Overview
Problem
Existing systems for determining the altitude of mobile devices, particularly in urban environments or buildings, suffer from imprecision due to unreliable reference-level pressure measurements, which can lead to significant errors in location estimation and delay emergency responses.
Innovation Solution
A method for improving altitude estimation using pressure data from less reliable sources, such as NOAA or crowdsourced weather stations, by employing four approaches: using threshold conditions, linear extrapolation, quadratic extrapolation, and decision tree regression to estimate or calibrate pressure sensors, ensuring accurate floor-level altitude determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reference-level pressure measurements from unreliable sources (NOAA or crowdsourced weather stations) are used, then altitude estimation becomes possible in areas without high-resolution reference data, but measurement precision deteriorates due to drift and inaccuracies in the pressure sensor readings
Solution Approach 1:
The system performs preliminary calibration of the mobile device pressure sensor by comparing readings against reference-level pressure data from reliable sources before actual altitude estimation. This preliminary calibration establishes a baseline correction factor that compensates for sensor drift and inaccuracies, enabling the system to adapt to unreliable reference sources while maintaining acceptable measurement precision through advance preparation and adjustment.
2Reliability
If pressure sensor calibration is performed using earlier reference-level pressure data, then the pressure sensor can be adjusted to compensate for drift, but the temporal resolution of available reference data limits the responsiveness of calibration updates
Solution Approach 1:
The system continuously monitors and compares mobile device pressure sensor readings against available reference-level pressure data, performing calibration updates in real-time rather than relying on periodic batch processing. This continuous approach maintains the useful action of calibration ongoing, allowing the system to respond immediately to changes in atmospheric conditions while compensating for temporal limitations through persistent comparison and adjustment mechanisms that operate throughout the measurement period.
3Measurement precision
If floor-level altitude accuracy is required, then precise pressure measurements are needed, but the system becomes more complex when dealing with multiple data sources and calibration methods
Solution Approach 1:
The system introduces an intermediary processing layer that standardizes and harmonizes pressure data from multiple sources (mobile device sensors, NOAA stations, crowdsourced weather stations) before using them for altitude estimation. This intermediary layer handles the complexity of data fusion, calibration, and quality filtering, presenting a simplified unified pressure profile to the altitude calculation algorithms. By isolating the complexity in the intermediary processing stage, the core altitude estimation function remains relatively simple while still achieving floor-level accuracy.
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
Enhances the accuracy of mobile device altitude estimation by effectively utilizing less reliable pressure data sources, achieving floor-level precision even when high-resolution reference-level pressures are unavailable, thereby improving location estimation and response times.
Implementation Method 1
altitude can be computed using a measurement of pressure from a pressure sensor of a mobile device along with ambient pressure measurement(s) from a calibrated network of reference pressure sensors, and a measurement of ambient temperature
Data Source
AI summary
Pressure-based estimation of a mobile device altitude or calibration of a pressure sensor involves machines that determine if a reference-level pressure value based on one or more measurements of pressure from a network of weather stations should or should not be used to calibrate a pressure sensor of a mobile device or to estimate an altitude of the mobile device. If the reference-level pressure value should be used, the reference-level pressure value is used to calibrate a pressure sensor of a mobile device or to estimate an altitude of the mobile device. If the reference-level pressure value should not be used, a trend in pressure is determined, an estimated reference-level pressure value based on the trend is determined, and the estimated reference-level pressure value is used to calibrate a pressure sensor of a mobile device or to estimate an altitude of the mobile device.