Radar Pointing Angle Refinement via Sideslip Compensation
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Solution Overview
Problem
Existing radar tracking devices in vehicles lack the ability to accurately determine the orientation or pointing angle of moving objects, which is crucial for collision avoidance and automated vehicle control, as they assume the orientation is aligned with the velocity vector of the object's centroid.
Innovation Solution
A method involving a processor that determines an initial pointing angle, estimates the position of a selected feature, calculates the velocity vector, lateral acceleration, and sideslip angle, and iteratively refines the pointing angle until convergence is achieved, using a combination of radar data and vehicle dynamics models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If radar tracking devices assume the orientation is aligned with the velocity vector of the object's centroid, then the device complexity is reduced and ease of operation is improved, but the measurement precision of the pointing angle deteriorates
Solution Approach 1:
The patent replaces the simple geometric assumption (velocity vector alignment) with a physics-based vehicle dynamics model that incorporates lateral acceleration, yaw rate, and sideslip angle calculations. This substitution of the measurement approach resolves the contradiction by providing accurate pointing angle determination without requiring complex additional sensors.
Solution Approach 2:
The patent introduces an intermediary computational process that uses radar-measured lateral acceleration and yaw rate to calculate the sideslip angle, which then corrects the pointing angle. This intermediary calculation layer bridges the gap between simple radar measurements and accurate orientation determination.
2Device complexity
If a simple velocity vector alignment assumption is used, then the device complexity is reduced, but the measurement precision of the pointing angle deteriorates
Solution Approach 1:
The patent replaces the simple geometric assumption (velocity vector alignment) with a physics-based vehicle dynamics model that incorporates lateral acceleration, yaw rate, and sideslip angle calculations. This substitution of the measurement approach resolves the contradiction by providing accurate pointing angle determination without requiring complex additional sensors.
Solution Approach 2:
The patent transforms the measurement approach by changing from direct geometric alignment assumption to a multi-parameter calculation involving lateral acceleration, yaw rate, and sideslip angle. This parameter transformation enables accurate pointing angle measurement while keeping the radar hardware simple.
3Measurement precision
If iterative refinement of the pointing angle is performed, then the measurement precision is improved, but the loss of time increases due to multiple calculation cycles
Solution Approach 1:
The patent implements an iterative refinement process where the calculated pointing angle is fed back to update the estimation of the selected feature's position, which in turn refines the lateral acceleration calculation. This feedback loop continues until convergence, ensuring high measurement precision while managing computational time through systematic iteration.
Solution Approach 2:
The patent merges multiple calculation steps (position estimation, velocity vector determination, lateral acceleration calculation, sideslip angle computation) into a unified iterative process. This integration allows the system to achieve high precision through coordinated refinement of all parameters simultaneously rather than through separate sequential operations.
Data Source
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AI summary
An illustrative example method of tracking a moving object includes determining an initial pointing angle of the object from a tracking device, determining an estimated position of a selected feature on the object based upon the initial pointing angle, determining a velocity vector at the estimated position, determining a lateral acceleration at the estimated position based upon the velocity vector and a yaw rate of the object, determining a sideslip angle of the selected feature based on the lateral acceleration, and determining a refined pointing angle of the object from the determined sideslip angle.