Wireless Terminal Elevation Estimation Using Barometric Bias Correction
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Solution Overview
Problem
Existing methods for estimating the elevation of a wireless terminal using barometric pressure measurements from airport pressure stations are inaccurate due to measurement bias errors and unreliable reference elevations, particularly in contexts requiring high precision such as emergency responses.
Innovation Solution
A location engine generates an estimate of the measurement bias of the barometric sensor at an airport pressure station by comparing outdoor pressure measurements from already-calibrated pressure stations, allowing for calibrated pressure measurements and improved elevation estimation of wireless terminals without direct access or calibration of the airport pressure station.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If barometric pressure measurements from airport pressure stations are used to estimate elevation, then elevation estimation is enabled, but measurement bias errors reduce accuracy
Solution Approach 1:
A location server acts as an intermediary to receive pressure measurements from both the wireless terminal and airport pressure station, process the data, and generate corrected elevation estimates. The server mediates between the unreliable airport pressure reference and the terminal measurement, applying bias correction algorithms to produce accurate results without requiring direct access to or calibration of the airport pressure station.
Solution Approach 2:
The system changes the parameter of pressure reference by using multiple pressure measurements (from both the terminal and airport station) and applying mathematical transformations. By calculating the difference between expected and actual pressure readings at the airport station, the system derives a bias correction parameter that is then applied to the terminal's pressure measurement to improve elevation accuracy.
2Ease of operation
If airport pressure stations are used as pressure references, then elevation data can be obtained, but direct access and calibration of these stations is not feasible
Solution Approach 1:
The airport pressure station effectively performs self-service by providing its pressure measurements to the location server, which then uses these measurements to calculate and apply bias corrections. The system leverages the existing operational data from the airport station without requiring physical access or manual calibration, allowing the station to serve its calibration purpose through its normal measurement function.
Solution Approach 2:
The system implements a feedback mechanism where the airport pressure station's measurements are continuously monitored and used to calculate bias corrections. The location server receives ongoing pressure data from the airport station, processes it to determine measurement biases, and applies these corrections to terminal elevation estimates, creating a continuous improvement loop that enhances accuracy over time.
3Device complexity
If traditional barometric pressure methods are used, then elevation estimation is simple, but precision is insufficient for emergency response applications
Solution Approach 1:
The system merges multiple data sources and processing functions into a unified location server. It combines pressure measurements from the wireless terminal, airport pressure station, and barometric model predictions, then integrates these through coordinate transformation algorithms and bias correction procedures. This consolidation of multiple elements into a single processing system achieves high precision without distributing complexity across multiple devices.
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 accurate elevation estimates of wireless terminals by compensating for measurement bias, enhancing precision and accessibility of location data, even in situations where traditional methods fall short.
Implementation Method 1
Some of these techniques rely on the relationship between barometric pressure, POBJ, and elevation, ZOBJ
Data Source
AI summary
A location engine that estimates the barometric pressure measurement bias of a pressure station reference, such as an airport pressure station, resulting in an improved estimate of the elevation of a wireless terminal. The location engine generates the estimate of bias of barometric pressure by comparing i) the outdoor barometric pressure measured by the airport pressure station at its unknown height above mean sea level (MSL) and ii) the expected outdoor barometric pressure derived from the pressure measurements from an already-calibrated pressure station, while accounting for the known height of the already-calibrated pressure station. The expected outdoor measurements correspond to a derived height above MSL of the airport pressure station. Subsequently, the location engine generates an estimate of the elevation of the wireless terminal by accounting for the estimate of measurement bias of the airport pressure station.