Radar Reflection Point Filtering via Adaptive Thresholds
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Solution Overview
Problem
Millimeter-wave radar systems generate noisy data due to refraction, reflection, and diffraction effects, as well as reflections from non-target objects, leading to reduced accuracy in vehicle target detection and trajectory monitoring.
Innovation Solution
A method and device for filtering radar reflection points by adjusting attribute thresholds based on clustering results and target detection processes, using attributes like radar cross-section (RCS), radial velocity (v), and received signal strength indicator (RSSI), to optimize noise filtering and target categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If millimeter-wave radar is used for target detection, then distance and speed measurements are accurate, but noisy data is generated due to refraction, reflection and diffraction effects
Solution Approach 1:
The patent extracts and removes noisy reflection points from the radar data by applying attribute thresholds (RCS, velocity, RSSI) to filter out invalid and false points generated by refraction, reflection and diffraction effects, keeping only the valid target reflection points
Solution Approach 2:
The patent implements feedback by using clustering results and target detection results to automatically adjust the attribute thresholds for filtering. The system continuously monitors the clustering quality and adjusts thresholds to optimize the balance between removing noise and preserving valid targets
2Ease of manufacture
If fixed attribute thresholds are used for filtering reflection points, then filtering process is simple, but noise filtering effectiveness is insufficient
Solution Approach 1:
The patent transforms the static fixed thresholds into dynamic adaptive thresholds that automatically adjust based on clustering results and target detection results. The thresholds for RCS, velocity, and RSSI are continuously optimized to match the actual traffic conditions and improve filtering effectiveness
Solution Approach 2:
The system performs self-optimization by using its own clustering results and target detection results to automatically adjust its filtering thresholds, eliminating the need for manual threshold tuning while improving adaptation to different traffic scenarios
3Measurement precision
If multiple attributes are used for filtering, then target categorization accuracy is improved, but device complexity increases
Solution Approach 1:
The patent segments the filtering process into distinct stages based on different attributes: first filtering by RCS to remove obvious noise, then by velocity to eliminate stationary objects, and finally by RSSI to remove weak signals. This segmented approach manages complexity by handling each attribute separately in sequence
Data Source
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AI summary
The present application discloses a method for filtering radar reflection points. The method comprises the following steps: filtering the initial reflection point set based on the attribute information of an initial reflection point set using one or more attribute thresholds to determine a plurality of reflection points belonging to the target range; clustering the plurality of reflection points belonging to the target range to determine the clustering results; adjusting the one or more attribute thresholds based on the clustering results and target detection results determined through a target detection process.