Adaptive GNSS Filter for Accurate Motion Path Tracking
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Solution Overview
Problem
Mobile device location determination technologies, such as those using GNSS data, often apply excessive smoothing, leading to inaccurate tracking of rapid changes in direction, resulting in a less accurate representation of the user's motion path, particularly in activities like running around loops or tracks.
Innovation Solution
A method and system that detect patterns in location data to adjust the parameters of a location data filter, ensuring that sharp corners and rapid changes in direction are not lost, by using pattern detection algorithms like the 'Tortoise and Hare' algorithm and contour analysis, allowing for real-time or post-processing adjustments to the filtering process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If strong smoothing is applied to GNSS data by the filter, then the position and velocity estimates become more stable, but rapid changes in direction are removed resulting in less accurate motion path estimation
Solution Approach 1:
The filter parameters are made dynamic rather than static. The system continuously monitors the motion path for sharp corners and rapid direction changes, then adjusts the smoothing strength in real-time. When sharp turns are detected, the filter applies less smoothing to preserve accuracy; when motion is smooth, it applies more smoothing to enhance stability.
Solution Approach 2:
The invention changes the filter parameters based on detected activity patterns. By analyzing the motion path for characteristics like sharp corners and rapid direction changes, the system modifies the filter's smoothing parameters to match the current motion regime, optimizing both stability and precision for different activity types.
2Measurement precision
If weak smoothing is applied to GNSS data, then rapid changes in direction are preserved, but the position and velocity estimates become less stable
Solution Approach 1:
The filter adapts its smoothing strength dynamically based on the detected motion characteristics. When the system detects sharp corners or rapid direction changes in the motion path, it automatically reduces smoothing to preserve accuracy. Conversely, during smooth motion segments, it increases smoothing to enhance stability, thus resolving the contradiction between preserving detail and maintaining stability.
Solution Approach 2:
The system modifies the filter parameters based on activity detection results. By identifying patterns such as sharp turns and rapid direction changes, the invention adjusts the smoothing parameters to match the current motion regime, achieving optimal balance between precision and stability for different activity types.
3Device complexity
If fixed filter parameters are used for all activities, then the system is simple to implement, but the motion path accuracy deteriorates during activities with sharp corners and rapid direction changes
Solution Approach 1:
The system performs preliminary analysis of the motion path to detect sharp corners and rapid direction changes before final filtering is applied. By pre-identifying activity patterns and motion characteristics, the system can proactively adjust filter parameters to ensure accuracy is maintained during critical segments of the motion path.
Solution Approach 2:
The invention implements a feedback mechanism where the detected motion path characteristics are fed back to adjust the filter parameters. The system continuously monitors for sharp corners and rapid direction changes, then uses this information to modify the smoothing strength, creating a closed-loop system that adapts to maintain accuracy across different activity types.
Data Source
AI summary
The disclosed embodiments are directed to detecting a user activity based on patterns in location data. In an embodiment, a method comprises: obtaining, by a processor of a computing device, location data; detecting, by the processor, a pattern in the location data; determining, by the processor and based on the detected pattern, an activity associated with a mobile device; and adjusting, by the processor, one or more parameters of a location data filter configured to process the location data.