GPS Trajectory Shape Filter for Inaccurate Sample Removal
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Solution Overview
Problem
Current position and orientation determination systems in vehicles face challenges such as multipath errors, noise in measurement signals, and drift in IMU signals, which affect the accuracy of GPS and IMU data, especially when combined for precise location and orientation calculations.
Innovation Solution
A method and system that utilize an off-line processor to remove inaccurate GPS samples using non-linear classification and correction mechanisms, including a global, recursive, and self-adaptive shape filter, to improve the accuracy of position and orientation calculations by distinguishing between accurate and inaccurate GPS samples and correcting IMU signals for drift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Kalman filters and statistical methods are used to compensate for drift and remove noise, then measurement accuracy is improved, but the system cannot effectively handle offsetted GPS signals with long time constant shifts
Solution Approach 1:
The patent segments GPS measurements into accurate and inaccurate samples using a shape filter that analyzes the geometric consistency of the vehicle trajectory. By dividing the measurement data into segments based on trajectory plausibility, the system can process accurate samples with standard filters while identifying and excluding inaccurate offsetted samples, thereby resolving the contradiction between noise removal effectiveness and reliability on offsetted signals.
Solution Approach 2:
The patent introduces an intermediary shape filter that acts as a mediator between raw GPS measurements and the final position/orientation calculation. This shape filter analyzes trajectory geometry and identifies plausible vs. implausible measurement sequences, serving as an intermediary layer that prevents offsetted GPS signals from corrupting the final result while allowing accurate signals to pass through for processing.
2Measurement precision
If GPS and IMU data are integrated for precise location and orientation, then measurement accuracy is improved, but multipath errors and signal noise affect the combined measurements
Solution Approach 1:
The patent converts the harmful effect of multipath errors and noise into a benefit by using the shape filter to identify trajectories that are geometrically plausible despite measurement errors. The system uses the known vehicle dynamics and trajectory geometry to distinguish between actual position changes and errors caused by multipath effects, thereby converting noisy measurements into reliable position and orientation data.
Solution Approach 2:
The patent changes the parameter being analyzed from raw GPS position coordinates to trajectory geometry parameters (curvature, length, direction changes). By transforming the measurement data into geometric parameters and comparing them against expected vehicle behavior, the system can filter out multipath errors and noise that distort position coordinates but preserve overall trajectory geometry.
3Measurement precision
If IMU drift is corrected using GPS samples, then position accuracy is improved, but inaccurate GPS samples can contaminate the IMU correction
Solution Approach 1:
The patent applies preliminary action by using the shape filter to identify and exclude inaccurate GPS samples before they are used for IMU drift correction. The system performs trajectory plausibility analysis in advance, creating a filtered set of only accurate GPS measurements that are then used for the drift correction process, preventing contamination from offsetted signals.
Solution Approach 2:
The patent implements feedback by using the shape filter to continuously monitor trajectory plausibility and provide feedback on which GPS samples are accurate vs. inaccurate. This feedback mechanism allows the system to dynamically select only reliable GPS samples for IMU correction, creating a closed-loop system that prevents contamination while maintaining correction accuracy.
Data Source
AI summary
Method of and apparatus for determining inaccurate GPS samples in a set of GPS samples, according to the following actions: a) obtaining GPS samples as taken by a global positioning system on board a vehicle when traveling along a trajectory; b) obtaining a first estimation of the trajectory based on the GPS samples; c) obtaining a second estimation of the trajectory at least based on measurements made by an inertial measurement unit on board vehicle when traveling along the trajectory; d) comparing the first and second estimations; e) establishing locations where the first estimation shows a variation compared with the second estimation above a predetermined threshold; f) if no such locations can be established continue with action j), otherwise continue with action g); g) removing GPS samples associated with the locations of high variation as being inaccurate GPS samples, thus forming a set of remaining GPS samples; h) calculating the first estimation anew of the trajectory based on the remaining GPS samples and calculating the second estimation anew; i) repeating actions d) to h); j) ending the actions.