Elevation-Assisted Location Estimation Using DEM Filtering
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Solution Overview
Problem
Existing location determination technologies for mobile devices, especially those using GNSS, face challenges in accuracy when fewer or low-quality satellite pseudorange measurements are available, and can be influenced by inaccurate digital elevation model (DEM) data, particularly in scenarios like bridges and tunnels.
Innovation Solution
A tightly-coupled elevation-assisted location estimation method is implemented, which uses DEM elevation measurements and GNSS data within a Kalman filter to improve location accuracy. This method filters DEM data based on bridge and tunnel events to ensure more accurate elevation assistance, enhancing the estimation process even with limited satellite data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If DEM elevation data is used to assist location estimation, then location accuracy is improved, but measurement faults can occur when mobile device is on bridges or in tunnels
Solution Approach 1:
The system continuously monitors the consistency between DEM elevation data and actual mobile device elevation (derived from GNSS and motion data). When inconsistencies are detected (such as impossible elevation changes), the system provides feedback to filter or exclude the DEM data, preventing faulty measurements from degrading location accuracy.
Solution Approach 2:
The elevation assistance system dynamically adjusts its behavior based on detected conditions. When bridge or tunnel events are detected through analysis of elevation changes and motion data, the system switches from using DEM data to alternative elevation sources, adapting to the changing measurement environment to maintain reliability.
2Measurement precision
If fewer GNSS satellites are available, then location determination becomes more difficult, but adding DEM elevation data can compensate
Solution Approach 1:
The system merges multiple elevation sources (DEM data, GNSS altitude, and motion-compensated elevation) into a unified elevation estimate. This combination allows the system to maintain accurate location determination even when satellite measurements are limited, by compensating with alternative elevation information from other sources.
3Measurement precision
If DEM elevation data is always used, then location estimation is enhanced, but inaccurate DEM data can degrade performance
Solution Approach 1:
The system continuously monitors the consistency between DEM elevation data and actual mobile device elevation (derived from GNSS and motion data). When inconsistencies are detected (such as impossible elevation changes), the system provides feedback to filter or exclude the DEM data, preventing faulty measurements from degrading location accuracy.
Solution Approach 2:
The system performs preliminary analysis of DEM data before using it for location estimation. By detecting bridge and tunnel events in advance through motion data and elevation change analysis, the system can prepare to switch to alternative elevation sources, preventing inaccurate DEM data from being applied and degrading performance.
Data Source
AI summary
In some implementations, a tightly-coupled elevation-assisted location estimate can be calculated based on digital elevation model (DEM) elevation measurements and global navigation satellite system (GNSS) data. The DEM elevation measurements and GNSS data can be provided as input to an estimator (e.g., a Kalman filter) to calculate an estimated geographic location of a mobile device. In some implementations, DEM elevation data can be filtered before being provided to the Kalman filter for estimating the geographic location of the mobile device. The DEM elevation data can be filtered in response to detecting bridge and/or tunnel events that indicate that the DEM elevation data does not accurately represent the actual elevation of the mobile device.