Barometric Sensor Bias Estimation Using GNSS Elevation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wireless terminal location estimation methods, particularly those relying on barometric pressure measurements, are hindered by sensor bias and noisy GPS signals, leading to inaccurate elevation estimates, especially in indoor environments where GPS signals are attenuated.
Innovation Solution
A location engine that estimates and compensates for the pressure measurement bias of a wireless terminal's barometric sensor using GPS-based elevation estimates and Kalman filtering, allowing for improved elevation determination by combining noisy GPS data with barometric pressure measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If barometric pressure measurements are used to estimate elevation, then elevation estimation is possible, but measurement bias and uncertainty lead to inaccurate results
Solution Approach 1:
The patent applies feedback by using GPS-based elevation estimates to continuously correct and update the barometric pressure measurements. The system compares the barometric elevation estimate with the GPS elevation estimate and uses the difference (bias) to adjust future barometric measurements, thereby reducing systematic errors and improving accuracy over time.
Solution Approach 2:
The patent introduces an intermediary approach by using GPS signals as a reference to calibrate the barometric sensor. The GPS-based elevation serves as a mediator that allows the system to determine and compensate for the barometric sensor's bias, effectively transferring the reliability of GPS (when available) to improve barometric measurement accuracy.
2Measurement precision
If GPS signals are used for location estimation, then location can be determined, but signal attenuation and noise result in poor accuracy in indoor environments
Solution Approach 1:
The patent merges the barometric pressure measurement system with the GPS-based location system. By combining the complementary strengths of both systems—barometric pressure providing stable elevation data indoors and GPS providing outdoor reference—the system achieves improved location estimation accuracy across diverse environments, particularly indoors where GPS alone fails.
3Measurement precision
If barometric pressure measurements are used for elevation estimation, then elevation can be determined, but environmental factors and signal variations cause significant measurement errors
Solution Approach 1:
The patent implements dynamics by making the elevation estimation system adaptive and adjustable. The system dynamically switches between using barometric pressure measurements and GPS-based estimates based on environmental conditions and signal availability. It continuously updates the bias correction factors based on real-time comparisons between the two measurement sources, allowing it to adapt to varying environmental factors such as temperature, humidity, and atmospheric conditions.
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
This approach provides a more accurate estimation of wireless terminal elevation by accounting for measurement bias and uncertainty, enhancing location-based applications such as E911 services by reducing errors associated with direct GPS-based estimates.
Implementation Method 1
a barometric sensor device, such as a wireless terminal, is provided with a pressure measurement at a location of interest
Implementation Method 2
by applying a Kalman filter having a current state and a previous state that exists prior to the current state
Data Source
AI summary
A method for estimating the pressure measurement bias of a barometric sensor in a wireless terminal. A location engine using the method generates an enhanced estimate of the measurement bias. The location engine generates the enhanced estimate based in part on Global Navigation Satellite System (GNSS)-based estimates of the elevation of the wireless terminal, which the terminal generates as it concurrently makes barometric pressure measurements. Each GNSS-based estimate of elevation is often generated from noisy measurements and has an associated uncertainty. The location engine accounts for the uncertainty in the GNSS estimates of elevation by applying an optimal estimation technique, such as Kalman filtering, on the biased pressure measurements and the GNSS-based estimates. Once the location engine generates the enhanced estimate of measurement bias, it can adjust subsequent measurements of barometric pressure made by the wireless terminal and generate improved estimates of elevation of the wireless terminal.