Moving Object Pointing Angle Determination via Trajectory Curvature
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Solution Overview
Problem
Existing radar tracking systems for vehicles lack the ability to accurately determine the orientation or pointing angle of moving objects, which is crucial for collision avoidance and autonomous vehicle control, as they assume the orientation is aligned with the velocity vector of the object's centroid.
Innovation Solution
A method and system that utilize a tracking device with a processor to calculate the pointing angle of a moving object by determining the product of trajectory curvature and distance, combined with a low-pass filter, to provide a more accurate estimation of the object's orientation, improving object tracking and vehicle control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If radar tracking devices assume orientation is aligned with velocity vector of centroid, then device complexity is reduced, but measurement precision of pointing angle deteriorates
Solution Approach 1:
The patent segments the object tracking into multiple reference points (at least two) distributed across the object rather than using a single centroid. This segmentation allows calculation of orientation through the geometric relationship between multiple points, improving pointing angle measurement precision while maintaining reasonable device complexity through software-based processing.
Solution Approach 2:
The patent transitions from one-dimensional centroid velocity vector assumption to two-dimensional spatial relationship analysis by using coordinates of multiple reference points. This dimensional change enables accurate orientation determination through geometric calculations (slopes, angles) between points, resolving the contradiction between simplicity and precision.
2Measurement precision
If multiple reference points are used to determine pointing angle, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary computational approach using geometric relationships (slopes, angles, perpendicular bisectors) as mediators between the reference points and the final orientation calculation. This intermediary mathematical framework enables high measurement precision through systematic processing of multiple points without requiring complex hardware modifications.
Solution Approach 2:
The patent replaces potential mechanical complexity with computational mathematics. Instead of using complex mechanical orientation sensors or multiple physical measurement devices, the solution uses mathematical algorithms (slope calculations, angle computations, perpendicular bisector intersections) to derive orientation from positional data of multiple reference points.
3Measurement precision
If pointing angle is accurately determined using trajectory curvature and distance, then measurement precision improves, but loss of time increases due to additional calculations
Solution Approach 1:
The patent performs preliminary calculations of trajectory curvature and distance metrics from the collected reference point data before computing the final pointing angle. By pre-calculating these intermediate values (curvature κ, distance d), the system optimizes the computational flow, reducing the time penalty of additional calculations while maintaining high measurement precision.
Solution Approach 2:
The patent maintains continuous tracking and calculation by using the most recent reference point positions and updating the pointing angle determination in a continuous manner. This continuous action approach ensures that the system processes data efficiently over time, minimizing computational delays while providing updated orientation information.
Data Source
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AI summary
An illustrative example method of tracking a moving object includes determining a heading angle of a centroid of the object from a tracking device, determining a raw difference value corresponding to a difference between a pointing angle of a selected feature on the object and the heading angle, wherein the raw difference is based on a trajectory curvature of the centroid from the tracking device and a distance between the centroid and the selected feature, determining a filtered difference between the pointing angle and the heading angle using a low pass filter, and determining the pointing angle by subtracting the filtered difference from the heading angle.