IMU-Based Vehicle Tracking via Heading Angle Map Matching
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Solution Overview
Problem
Existing vehicle tracking systems rely on geolocation data from GNSS services, which can be jammed or unavailable due to theft or environmental conditions, making it difficult to track vehicles without continuous and accurate motion data.
Innovation Solution
A method and system using IMU data to accumulate motion data, compute heading angle time-series, analyze map data to identify candidate routes, and rank them based on match with simulated heading angle time-series to estimate vehicle location, even without geolocation services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If geolocation data from GNSS services is used for vehicle tracking, then tracking accuracy is improved, but the system becomes vulnerable to jamming and unavailability
Solution Approach 1:
The patent introduces an intermediary system that uses IMU sensors and map data as a mediator between the vehicle and the tracking server. When GNSS is unavailable, the IMU-based inertial navigation system computes vehicle position, orientation, and speed by integrating motion data, serving as a reliable alternative that maintains tracking continuity without direct dependency on satellite signals
Solution Approach 2:
The system changes the measurement parameters from direct geolocation coordinates (GNSS) to motion-based parameters (acceleration, angular velocity) captured by IMUs. By integrating these parameters over time and comparing with map data, the system derives position information that is independent of satellite signal availability, thus resolving the contradiction between accuracy and reliability
2Reliability
If IMU motion data is used for vehicle tracking without geolocation services, then system reliability is improved, but measurement precision deteriorates
Solution Approach 1:
The system maintains continuous tracking by continuously integrating IMU motion data (acceleration and angular velocity) to compute vehicle state. This continuous integration process, combined with periodic comparisons to map data, ensures uninterrupted tracking availability while maintaining acceptable precision through constant motion analysis rather than intermittent geolocation fixes
Solution Approach 2:
The patent replaces the electronic/GNSS-based positioning system with a mechanics-based inertial navigation system using IMUs. By substituting satellite signal dependency with physical motion sensing and mathematical integration, the system achieves reliability in GNSS-denied environments while using map matching algorithms to maintain positioning precision
3Reliability
If motion data is accumulated and processed to estimate vehicle location, then tracking robustness is improved, but device complexity increases
Solution Approach 1:
The system segments the tracking functionality into distinct modules: IMU data acquisition, motion integration computation, map data analysis, candidate route identification, and location estimation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture by dividing the complex task of inertial navigation into manageable, modular functions that can be processed sequentially
Data Source
AI summary
A method of tracking ground vehicles based on Inertial Measurement Unit (IMU) data, comprising accumulating motion data captured by IMU(s) deployed in a vehicle, the motion data captured periodically in a plurality of time points starting from a known initial location of the vehicle comprises at least acceleration and angular velocity, computing, based on the motion data, a measured heading angle time-series of the vehicle expressing a movement direction of the vehicle over time, analyzing map data of an area of the known initial location to identify candidate routes, simulating, based on the map data, a simulated heading angle time-series for each candidate route expressing a simulated movement direction along the respective candidate route over time, ranking the candidate routes based on match between each simulated heading angle time-series and the measured heading angle time-series, and estimating a location of the vehicle based on the ranking.