Radar Sensor Computing Device Disruption Detection
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Solution Overview
Problem
Existing radar sensors struggle to consistently detect and characterize disruptions caused by blocking or interference, leading to increased noise and reduced object detection capabilities in automated vehicles.
Innovation Solution
A computing device with a computing module that processes radar data to determine characteristic figures for noise distributions, allowing for the identification and differentiation between blocking and interference disruptions by analyzing noise levels and leakage signals, and updating reference noise distributions for precise disruption detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing radar sensor disruption detection methods are used, then either interference or blocking can be detected, but consistent characterization of disruptions is not possible
Solution Approach 1:
The patent changes the parameter of noise distribution analysis by determining characteristic figures for specific quantiles (5th, 16th, 50th percentiles) of the noise distribution. This allows consistent characterization of disruptions by comparing how different quantiles are affected, enabling reliable distinction between blocking and interference scenarios through parameter-based analysis rather than single-threshold detection
Solution Approach 2:
The patent segments the noise distribution into different quantile ranges (5th percentile for blocking, 16th percentile for interference, 50th percentile for comparison) to analyze different aspects of disruption impact. By dividing the detection approach into multiple statistical segments, the system achieves consistent characterization of different disruption types that cannot be detected by single-method approaches
2Measurement precision
If disruption detection sensitivity is increased, then more disruptions are identified, but false detections increase
Solution Approach 1:
The patent implements feedback by comparing characteristic figures from multiple quantiles against each other and against threshold values. The system uses the 50th percentile as a reference point and compares it with the 5th and 16th percentiles, creating a feedback mechanism that adjusts detection decisions based on relative changes rather than absolute thresholds, reducing false detections while maintaining sensitivity
Solution Approach 2:
The patent applies partial action by using only specific quantiles (5th, 16th, 50th) of the noise distribution rather than analyzing the entire distribution. This selective approach focuses computational resources on the most informative segments of the noise distribution, achieving high detection sensitivity without the computational overhead and false positive rates associated with comprehensive analysis
Data Source
AI summary
A computing device for a radar sensor for identifying a disruption of the radar sensor through blocking or interference includes at least one interface for receiving radar data from the radar sensor and at least one processing device configured to determine at least one characteristic figure for a range of values in a noise distribution in the radar data, in which at most few and/at most weakly reflecting objects are present, and evaluate the characteristic figure in order to identify a disruption of the radar sensor.