Vehicular Radar Calibration for Azimuth and Elevation Misalignment
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Solution Overview
Problem
Existing vehicle radar systems face challenges in accurately calibrating azimuth and elevation misalignments due to installation tolerances, which are difficult to measure mechanically and require expensive equipment, impacting the performance of advanced driver assistance systems (ADAS).
Innovation Solution
A radar calibrator system that uses post-processing of radar data from an object moved along the vehicle's axis to determine and correct azimuth and elevation misalignments, utilizing both physical and virtual targets to calibrate radar sensors without expensive mechanical measurement equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mechanical calibration methods are used to achieve precise radar sensor alignment, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical calibration equipment with a computational approach. A data processor captures images of a calibration object at multiple known positions and uses image processing algorithms to calculate misalignment parameters. This substitutes mechanical measurement systems with an optical-digital-computational system, reducing mechanical complexity while maintaining or improving precision.
Solution Approach 2:
The patent uses digital images (copies) of the calibration object captured by the radar sensor at different positions to determine misalignment. Instead of directly measuring physical dimensions with mechanical tools, the system creates and analyzes digital representations, enabling precise calculation of alignment parameters through computational methods rather than direct mechanical measurement.
2Manufacturing precision
If expensive mechanical calibration equipment is used, then manufacturing precision is improved, but ease of manufacture deteriorates
Solution Approach 1:
The patent replaces expensive mechanical calibration equipment with a software-based solution running on standard computing hardware. The data processor uses image processing and mathematical calculations to determine misalignment parameters, eliminating the need for specialized mechanical instruments and making the calibration process accessible with conventional equipment.
Solution Approach 2:
The calibration system uses the radar sensor's own imaging capability to perform self-calibration. By capturing images of a calibration object at known positions and processing these images computationally, the system determines its own misalignment parameters without requiring external specialized calibration equipment, enabling the system to calibrate itself using existing resources.
3Measurement precision
If traditional calibration methods are used, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent captures multiple images of the calibration object at different positions in sequence, continuously gathering data to improve measurement accuracy. By collecting data from multiple positions and processing all images together, the system achieves higher precision through accumulated information while the calibration process remains efficient and uninterrupted.
Solution Approach 2:
The patent uses a calibration object with pre-established known positions and dimensions. The geometric relationships are predetermined, allowing the data processor to directly calculate misalignment parameters from captured images without requiring complex real-time measurements or iterative adjustments, significantly reducing calibration time while maintaining precision.
Data Source
AI summary
A method for calibrating a radar system includes disposing the radar sensor at a vehicle and disposing an object at a first location relative to the vehicle. Using sensor data captured by the radar sensor with the object at the first location, a first location of the object relative to the radar sensor is determined. The object is moved along an intended principal axis of sensing for the radar sensor from the first location to a second location. Using sensor data captured by the radar sensor with the object at the second location, a second location of the object relative to the radar sensor is determined. Using the first determined location of the object and the second determined location of the object, a misalignment of the radar sensor is determined. The radar system is calibrated based at least in part on the misalignment.