Radar Interference Suppression Using Adaptive Spectrogram Scaling
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Solution Overview
Problem
Radar systems face interference from other sources, which degrades detection performance, particularly in congested spectrum environments, leading to inaccurate target characterization and increased false positives.
Innovation Solution
Implementing interference suppression techniques that involve identifying interfered cells in a spectrogram using frequency-specific thresholds and applying adaptive scaling factors to reduce interference magnitudes, rather than zeroing them out, thereby generating interference-suppressed samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional interference cancellation techniques are used to remove interference components, then interference levels are reduced, but ghost artifacts are generated causing false positive peak detections
Solution Approach 1:
The patent converts the harmful interference signal into a beneficial element by using it to update the interference model. The interference canceller utilizes the identified interference components to improve the interference model, which then enables more accurate suppression of interference in subsequent processing, reducing ghost artifacts while maintaining interference suppression.
Solution Approach 2:
The system implements feedback by continuously updating the interference model based on detected interference components. The interference model is refined using feedback from the interference canceller's analysis of ADC samples, creating a closed-loop system that progressively improves interference suppression accuracy and reduces false detections.
2Productivity
If radar systems operate in congested spectrum environments, then more targets can be detected, but detection performance deteriorates due to radar-to-radar interference
Solution Approach 1:
The patent segments the signal processing into distinct stages: initial ADC sampling, interference identification, interference model updating, and refined interference suppression. This segmentation allows the system to handle interference systematically at different processing stages, maintaining detection capacity in congested environments while preserving measurement precision through targeted interference suppression.
Solution Approach 2:
The system performs preliminary interference identification and model updating before final target detection and characterization. By pre-processing the ADC samples to identify and model interference components beforehand, the system prepares a cleaned signal foundation that enables accurate target detection even in congested spectrum environments.
3Object-affected harmful factors
If interference components are zeroed out in received radar signal data, then interference is eliminated, but magnitude of spurious sidelobes increases creating ghost artifacts
Solution Approach 1:
Instead of binary zeroing of interference components, the patent applies adaptive scaling factors that continuously adjust the magnitude of interference components based on the updated interference model. This parameter change from discrete zeroing to continuous scaling preserves signal integrity while suppressing interference, eliminating ghost artifacts by maintaining appropriate sidelobe magnitudes.
Data Source
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AI summary
Radar systems and interference suppression methods are described, including a radar system that includes communication circuitry configured to transmit radar signals and to receive reflections of the transmitted radar signals reflected by an object in an environment of the radar system and processing circuitry. The processing circuitry is configured to generate a spectrogram by converting samples of the reflections into a time-frequency domain, determine a plurality of interference thresholds, including a respective interference threshold for each frequency bin of the spectrogram, identify interfered cells of the spectrogram based on the plurality of interference thresholds, determine scaling factors for the interfered cells based on at least the plurality of interference thresholds and magnitudes of the interfered cells, generate an interference-suppressed spectrogram by applying the scaling factors to the interfered cells to reduce the magnitudes of the interfered cells, and generate interference-suppressed samples based on the interference-suppressed spectrogram.