Automotive Radar Interference Suppression Using Recursive Thresholding
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Solution Overview
Problem
Automotive radar systems face interference challenges due to multiple interference signals with varying power levels, leading to degraded detection performance as conventional thresholding techniques struggle to accurately suppress interference, often missing weak signals or misidentifying strong peaks.
Innovation Solution
Implementing a recursive thresholding process that utilizes threshold data from previous chirps, combined with a min-max operation, to determine a predicted threshold for current chirps, followed by an estimated threshold application for interference suppression, reducing latency and improving robustness in complex interference scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional thresholding techniques are used to suppress interference, then the radar system can process received signals, but weak interference signals are missed or strong peaks are misidentified, degrading detection performance
Solution Approach 1:
The patent implements a recursive thresholding process where the threshold for the current chirp is determined based on thresholds from previous chirps. The signal processor maintains a history of thresholds and uses this feedback to adaptively adjust the current threshold, allowing accurate suppression of interference signals with varying power levels while preserving weak signals and maintaining strong peak identification.
Solution Approach 2:
The patent transitions from static thresholding to dynamic adaptive thresholding. The threshold value changes over time based on the statistical properties of previous chirps, allowing the system to adapt to varying interference conditions. This dynamic approach enables the threshold to track interference power levels while maintaining detection accuracy across different scenarios.
2Measurement precision
If adaptive thresholding methods are implemented to improve interference suppression, then detection accuracy improves, but processing latency increases
Solution Approach 1:
The patent performs preliminary computations by maintaining a running history of thresholds from previous chirps. This pre-computed threshold history is readily available when processing the current chirp, eliminating the need for extensive retrospective analysis. The signal processor can quickly determine the current threshold using the pre-established recursive relationship, reducing processing latency while maintaining high detection accuracy.
3Device complexity
If simple thresholding is used to reduce processing complexity, then computational load is low, but interference suppression effectiveness deteriorates in complex interference scenarios
Solution Approach 1:
The patent segments the interference suppression process into manageable computational steps: (1) storing thresholds from previous chirps in a history buffer, (2) computing the current threshold using a recursive formula based on the threshold history, and (3) applying the threshold to suppress interference in the current chirp. This segmentation maintains low computational complexity at each step while achieving effective interference suppression through the cumulative effect of the recursive process.
Data Source
AI summary
The present disclosure relates to a radar system having signal processing circuitry configured to receive sets of samples each representing a respective reflected radar signal and to generate sets of interference-suppressed samples including a first set of interference-suppressed samples corresponding to a first reflected radar signal represented by a first set of samples. To generate the first set of interference-suppressed samples, the signal processing circuitry may determine a first threshold based on at least one previously determined threshold for at least a second set of samples of the sets of samples, determine a second threshold based on the first set of samples, determine a third threshold as an average of the first threshold and the second threshold, and apply the third threshold to the first set of samples to remove or suppress interference components of the first set of samples to generate the first set of interference-suppressed samples.