Radar Signal Interference Mitigation via Sparsity Recovery
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Solution Overview
Problem
Radar systems on vehicles face interference from other radar systems, leading to corrupted signals that mask targets, particularly due to synchronous and asynchronous interference, which existing technologies struggle to effectively detect and mitigate without prior knowledge of the interfering radar parameters.
Innovation Solution
A two-step approach is implemented to detect and mitigate interference, involving the detection of synchronous and asynchronous interference through frequency and time-domain analysis, followed by masking and recovery of corrupted samples to preserve existing targets and reject interference, using techniques such as Fourier-domain sparsity enforcement and Short Time Fourier Transform (STFT) without requiring knowledge of the interfering radar parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional interference mitigation techniques are used, then interference can be reduced, but target detection accuracy deteriorates due to smearing effects
Solution Approach 1:
The patent segments the interference mitigation process into distinct modules: synchronous interference detection using frequency-domain analysis, asynchronous interference detection using time-domain STFT, masking of corrupted samples, and sparsity-enforced recovery. This segmentation allows each component to address specific aspects of interference without compromising target detection accuracy.
Solution Approach 2:
The patent changes the domain parameters for analysis by transforming signals into frequency-domain representations and applying Short Time Fourier Transform in the time-frequency domain. This parameter transformation enables effective separation and mitigation of interference patterns while preserving target characteristics through domain-specific processing.
2Object-affected harmful factors
If interference is masked out, then interference effects are reduced, but target information is lost due to smearing
Solution Approach 1:
The patent discards masked-out corrupted samples containing interference and recovers the underlying target information through sparsity-enforced signal recovery algorithms. This two-step process of masking followed by intelligent recovery preserves target information while removing interference effects.
Solution Approach 2:
The patent replaces traditional mechanical masking approaches with computational signal processing techniques including frequency-domain analysis, time-frequency transforms, and sparsity-constrained optimization algorithms. This substitution enables more precise interference removal while preserving target information through mathematical modeling rather than simple signal truncation.
3Measurement precision
If sparsity enforcement is applied, then small targets are preserved, but computational complexity increases
Solution Approach 1:
The patent applies sparsity enforcement selectively to the recovered signal components rather than processing the entire signal uniformly. This partial application of sparsity constraints focuses computational resources on recovering only the corrupted portions while maintaining simplicity in processing already-clean signal segments.
Data Source
AI summary
Techniques to improve the detection and mitigation of synchronous and asynchronous interference in radar signals. The corrupted received signals can be processed to reduce the effect of interference, while preserving existing targets. Various can use a two-step approach to (1) detect and mask the corrupted samples, and (2) recover the masked-out samples. The recovery step enforces sparsity of the existing targets and prevents target smearing, which is a common problem after interference mitigation. The sparsity enforcing recovery step can preserve small targets while successfully rejecting interference. The techniques do not require any prior knowledge of the parameters of the interfering radar.