Vehicle GNSS Positioning With Multi-Model Motion State Interaction
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Solution Overview
Problem
Existing vehicle-mounted GNSS positioning systems face challenges in urban environments due to signal obstructions and multipath effects, leading to positioning errors of tens of meters or more, and traditional motion models fail to accurately describe complex vehicle motions, especially in turning states.
Innovation Solution
A vehicle-mounted GNSS positioning method using a multi-motion model interaction, incorporating a constant velocity (PCV) and constant steering angular velocity (PCSAV) models, combined with an interacting multiple model (IMM) for state estimation and error covariance, to improve accuracy and continuity by fusing state vectors and error matrices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a traditional single motion model (e.g., CV or CA) is used for vehicle positioning, then the system complexity remains low, but the positioning accuracy deteriorates in complex urban environments with multiple motion states
Solution Approach 1:
The patent divides the single motion model into multiple specialized motion models (CV model for constant velocity, CA model for constant acceleration, and CT model for constant turning). Each sub-model is optimized for specific motion states, allowing the system to achieve high positioning accuracy across diverse urban driving scenarios without excessive complexity through modular design
Solution Approach 2:
The patent dynamically changes the parameters of motion models based on vehicle state detection. By monitoring acceleration, velocity, and turning rate parameters, the system selects and switches between different motion models (CV, CA, CT) to match current vehicle behavior, thereby maintaining high positioning accuracy while adapting to varying motion conditions
2Reliability
If a single motion model is used to predict vehicle state, then the computational load is low, but the positioning continuity deteriorates when the vehicle transitions between different motion states
Solution Approach 1:
The patent implements a dynamic motion model selection mechanism that continuously monitors vehicle motion parameters (acceleration, velocity, turning rate) and adapts the active motion model according to current driving conditions. This dynamic adaptation ensures positioning continuity during motion state transitions while maintaining reasonable computational load through efficient model switching based on predefined thresholds
Solution Approach 2:
The patent employs feedback mechanisms where the system continuously evaluates vehicle state measurements against predicted states from motion models. When deviations exceed thresholds, the system triggers model switching or parameter adjustments, ensuring positioning continuity during transitions between different motion states while maintaining computational efficiency through event-driven updates
3Measurement precision
If FDE or robust estimation based on posterior residuals is used to handle gross errors, then the observation data filtering is improved, but false alarms increase due to error allocation among correlated observations
Solution Approach 1:
The patent performs gross error detection and exclusion in the observation domain before state estimation using preliminary statistical tests on observation residuals. By identifying and removing gross errors early in the processing chain, the system prevents error propagation to state estimates while reducing false alarms caused by correlated observation errors, thereby improving both measurement precision and reliability
Data Source
AI summary
A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.