Vehicle Navigation Using Radar Static Surface Velocity
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Solution Overview
Problem
Existing navigation systems rely heavily on inertial measurement units (IMUs) for dead reckoning, which are prone to accumulating errors due to double integration of acceleration data, leading to significant position drift over time.
Innovation Solution
The method involves accessing radar or electromagnetic sensor images to detect static surfaces, calculate linear velocity relative to these surfaces, and integrate this velocity with angular velocity and scan cycle duration to determine the change in vehicle position, thereby reducing reliance on IMUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If inertial measurement units (IMUs) are used for dead reckoning navigation, then navigation can be performed without external signals, but position accuracy deteriorates over time due to cumulative integration errors
Solution Approach 1:
The patent introduces radar-detected static surfaces as an intermediary reference frame to mediate between the vehicle's motion and the ground truth. By detecting static surfaces and calculating radial velocities relative to them, the system creates a new reference that eliminates the need for direct GPS signals while maintaining position accuracy, thus resolving the contradiction between navigation capability in GPS-denied zones and position calculation accuracy.
Solution Approach 2:
The patent replaces the mechanical inertial measurement system (IMU) with a radar-based optical/electromagnetic system. Instead of relying on mechanical accelerometers and gyroscopes that accumulate errors through double integration, the system uses radar to directly measure radial velocities of static surfaces, substituting the mechanical measurement approach with an electromagnetic one that provides more accurate and stable position calculations.
2Measurement precision
If radar sensor images are processed to detect static surfaces and calculate linear velocity, then position calculation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the radar image processing into distinct functional steps: detecting static surfaces, calculating radial velocities for each surface, determining vehicle linear velocity from the surface velocities, and integrating with angular velocity to get position changes. This segmentation allows each step to be optimized independently and reduces overall computational complexity by breaking down the complex navigation problem into manageable sub-problems.
Solution Approach 2:
The patent applies local quality by processing only the portions of the radar image that contain static surfaces and are relevant to the vehicle's motion estimation. Instead of processing the entire radar image uniformly, the system identifies and processes only the constellations of points representing static surfaces, thereby reducing computational load while maintaining position calculation accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the accuracy and reliability of position calculations by leveraging static surfaces in the environment, reducing cumulative positional error and maintaining accurate navigation even in GPS-denied zones.
Implementation Method 1
annotated with radial velocities of the set of surfaces relative to the radar sensor
Data Source
AI summary
One variation of a method includes: accessing an image generated by a sensor arranged on a vehicle, the image including a set of points representing positions and radial velocities of surfaces in a field of view of the sensor during a scan cycle; detecting a constellation of points, in the set of points in the image, representing a static surface in the field of view of the sensor; calculating a linear velocity of the vehicle relative to the static surface during the scan cycle based on radial velocities of the constellation of points; accessing an angular velocity of the vehicle during the scan cycle detected by a motion sensor arranged on the vehicle; and calculating a change in position of the vehicle during the scan cycle based on the linear velocity, the angular velocity, and a duration of the scan cycle.