Motion Track Reconstruction Using Filtered Positioning Curves
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Solution Overview
Problem
Existing vehicle positioning systems face challenges in accurately reconstructing motion tracks in areas with weak or no GPS signals, leading to temporary loss of positioning and drifting data, which affects navigation accuracy and user experience.
Innovation Solution
A method and apparatus that involve obtaining and filtering positioning data to obtain segments of first curves, using noise filtering techniques such as Kalman filtering, to determine a motion track, thereby reducing the impact of GPS noise and data loss, and reconstructing the track with improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS signals are used for vehicle positioning, then positioning can be implemented in most areas, but positioning accuracy deteriorates in areas with weak or no GPS signals such as tunnels or underground garages
Solution Approach 1:
The patent segments the positioning data into multiple components (GPS positioning data, inertial navigation data, map matching data) and processes each segment separately to compensate for the weaknesses of individual positioning methods in different environments
Solution Approach 2:
The patent combines multiple positioning data sources (GPS, inertial navigation, map matching) into a unified positioning result, allowing the system to maintain both availability and accuracy across different environments by leveraging the strengths of each method
2Quantity of substance
If positioning data is collected continuously to improve track reconstruction accuracy, then more positioning points are available, but noise and drifting data increase leading to lower accuracy
Solution Approach 1:
The patent extracts and removes noise and drifting data from the positioning data set through filtering algorithms, keeping only the valid positioning information that contributes to accurate track reconstruction
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously evaluates the quality of positioning data and adjusts the filtering and reconstruction process based on the detected noise levels and data validity
3Device complexity
If traditional curve fitting methods are used on all positioning data, then the process is simple, but the reconstructed track deviates from the actual vehicle path due to noise and data loss
Solution Approach 1:
The patent performs preliminary filtering and validation of positioning data before applying curve fitting methods, removing noise and identifying valid data points in advance to ensure that the subsequent reconstruction process works with high-quality input data
Solution Approach 2:
The patent applies different processing qualities to different portions of the positioning data, using more rigorous filtering and reconstruction methods for critical segments of the track while using simpler methods for less critical portions
Data Source
AI summary
A method for reconstructing a motion track applied to a terminal is provided. The method includes: obtaining a data set, the data set including positioning data obtained by positioning a target object; performing data fitting on target data in the data set to obtain a plurality of segments of first curves, the target data being positioning data obtained by performing noise filtering on the data set; and determining a motion track of the target object based on the plurality of segments of first curves. Counterpart apparatus and non-transitory computer-readable storage medium embodiments are also contemplated.