Adaptive Radar Tracking Filter Coefficients for Curve Stability
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Solution Overview
Problem
Radar-based moving object detection systems face challenges in maintaining tracking trajectory accuracy, particularly when the vehicle is in a curve or when lane changes occur, leading to rapid position changes and reduced responsiveness due to variations in observed point positions.
Innovation Solution
A moving object detection apparatus that dynamically adjusts tracking filter coefficients based on the distribution of observed points, setting a higher tracking degree when bias is detected and a lower degree otherwise, to improve responsiveness and reduce trajectory variation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If fixed tracking filter coefficients are used to maintain stable tracking, then tracking trajectory stability is improved, but responsiveness to rapid position changes deteriorates
Solution Approach 1:
The tracking filter coefficients are made dynamic by continuously adjusting them based on the distribution of observed points. The system transitions from fixed coefficients to adaptive coefficients that automatically change tracking strength according to current detection conditions, resolving the contradiction between stability and responsiveness.
Solution Approach 2:
The system changes the parameter of tracking filter coefficients based on the distribution characteristics of observed points. When bias is detected in observed point distribution, the coefficients are adjusted to increase tracking strength, thereby adapting the system behavior to current conditions and simultaneously achieving stability and responsiveness.
2Measurement precision
If tracking filter coefficients are increased to enhance tracking accuracy, then trajectory accuracy is improved, but responsiveness to rapid position changes deteriorates
Solution Approach 1:
The tracking filter coefficients are dynamically adjusted based on real-time analysis of observed point distribution. The system automatically increases coefficients when bias is detected to improve accuracy, and decreases them when rapid changes are detected, thereby achieving both high accuracy and responsiveness without manual intervention.
Solution Approach 2:
The system implements feedback by continuously monitoring the distribution of observed points and using this information to adjust tracking filter coefficients. This closed-loop control ensures that trajectory accuracy is improved when needed while maintaining responsiveness by reducing tracking strength when rapid position changes occur.
3Speed
If tracking filter coefficients are decreased to improve responsiveness, then responsiveness to position changes is improved, but tracking trajectory stability deteriorates
Solution Approach 1:
The system dynamically adjusts tracking filter coefficients based on the distribution of observed points. When the distribution indicates stability, coefficients are reduced to improve responsiveness. When bias is detected indicating instability, coefficients are increased to restore stability, thereby achieving both responsiveness and stability through adaptive control.
Solution Approach 2:
The tracking filter coefficients are changed based on the statistical distribution of observed points. The system analyzes whether observed points are biased and adjusts coefficients accordingly, allowing the system to switch between high responsiveness and high stability modes automatically based on current detection conditions.
4Stability of the object's composition
If high tracking filter coefficients are used to suppress trajectory variation, then trajectory stability is improved, but responsiveness to rapid position changes deteriorates
Solution Approach 1:
The tracking filter coefficients are made dynamic through continuous adjustment based on observed point distribution analysis. The system automatically adapts tracking strength to current conditions, suppressing trajectory variation when stable conditions exist while maintaining high responsiveness when rapid changes are detected, thereby resolving the contradiction between stability and productivity.
Solution Approach 2:
The system uses feedback from observed point distribution to automatically adjust tracking filter coefficients. This feedback mechanism ensures that trajectory stability is suppressed only when necessary, while responsiveness is maintained by reducing tracking strength when rapid position changes are detected, achieving both goals simultaneously.
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
The apparatus effectively suppresses variation in tracking trajectories when no rapid position change occurs and enhances responsiveness during rapid changes, such as lane changes, by adapting filter coefficients to the observed point distribution.
Implementation Method 1
a radar apparatus that is mounted to a vehicle to transmit and receive radar waves
Data Source
AI summary
A moving object detection apparatus repeatedly acquires, from a radar apparatus, observed-point information indicating observed-point positions that are positions of observed points where radar waves are reflected. The apparatus estimates, based on the observed-point positions indicated respectively by a plurality of pieces of the observed-point information and tracking filter coefficients indicating the degree of tracking the observed-point positions, a tracking trajectory tracking movement of a moving object corresponding to a plurality of the observed points. The apparatus determines whether distribution of the plurality of the observed points on both sides of the tracking trajectory is continuously biased to one side of the tracking trajectory. The apparatus sets the tracking filter coefficients so that the tracking degree is higher when the distribution of the plurality of the observed points is determined to be biased than when the distribution of the plurality of the observed points is determined to be not biased.