Radar Peak Detection Using Tracked Object Threshold Adaptation
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Solution Overview
Problem
Radar systems in vehicles face false negative detections due to signal strengths of radar reflections being close to the noise floor, leading to inaccurate peak detection in environments.
Innovation Solution
Adapting detection thresholds based on tracked object information to reduce the threshold in regions where objects are expected, enhancing peak detection sensitivity and accuracy for extended objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a constant detection threshold is used within each range bin, then the radar system maintains simple detection logic and computational efficiency, but false negative detections occur where signal strength is close to the noise floor
Solution Approach 1:
The patent applies local quality by making the detection threshold adaptive rather than constant. The threshold is adjusted locally for each range-Doppler bin based on tracked object information, allowing the system to lower thresholds in regions where objects are expected while maintaining higher thresholds elsewhere. This resolves the contradiction by improving detection accuracy in critical areas without uniformly increasing system complexity.
Solution Approach 2:
The patent uses preliminary action by utilizing tracked object information from previous time steps to predict where objects are likely to be in the current frame. This pre-computed object information is used to adjust detection thresholds before actual peak detection occurs, allowing the system to prepare adaptive thresholds in advance based on object trajectories and expected positions.
2Reliability
If the detection threshold is lowered to reduce false negatives, then detection sensitivity improves, but the number of false positive detections and clutter increases
Solution Approach 1:
The patent applies local quality by spatially selective threshold adjustment. Thresholds are lowered only in specific range-Doppler bins where tracked objects are expected to be located, while maintaining higher thresholds in other regions. This localized approach reduces false negatives in object regions without significantly increasing false positives and clutter in non-object regions.
Solution Approach 2:
The patent uses feedback by continuously updating detection thresholds based on tracked object information from previous frames. The system monitors object positions, velocities, and trajectories, and uses this feedback to dynamically adjust thresholds in the current frame, creating a closed-loop system that adapts to changing environmental conditions while maintaining detection accuracy.
3Measurement precision
If adaptive detection thresholds based on tracked object information are used, then detection sensitivity for extended objects improves, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing object information including position, velocity, and trajectory predictions before the actual peak detection process. This pre-processing allows the system to prepare adaptive thresholds in advance based on object expectations, reducing the computational burden during the critical detection phase while maintaining high sensitivity.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the detection threshold parameter based on tracked object information. Instead of changing the overall detection algorithm complexity, the system modifies the threshold parameter adaptively for different range-Doppler bins, achieving improved sensitivity through parameter optimization rather than algorithmic complexity increases.
Data Source
AI summary
A system includes a processor and a non-transitory computer-readable medium storing machine instructions that cause the processor to perform a first fast Fourier transform (FFT) on received radar data to obtain a range-antenna data array and to perform a second FFT on the range-antenna data array to obtain a range-Doppler-antenna data cube. The processor performs peak detection on the range-Doppler-antenna data cube based on object information to obtain a subset that includes confirmed peaks and candidate peaks. The processor performs angle of arrival calculations for the subset of the range-Doppler-antenna data cube, and filters candidate peaks in the subset to obtain a point cloud representative of an environment. In some implementations, the confirmed peaks are detected based on a default threshold value, the candidate peaks are detected based on at least one adapted threshold value, and the at least one adapted threshold value is determined based on the object information.