Radar Interference Mitigation via Low-Rank Sparse Decomposition
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Solution Overview
Problem
In-vehicle radar systems face significant challenges with interference, particularly in dense traffic situations, where multiple radar sensors operating in the same frequency bands lead to self-interference, cross-interference, and interference from other radars, resulting in ghost targets and increased noise floors.
Innovation Solution
The implementation of a low-rank recovery method motivated by group sparsity, combined with transformations like short-time Fourier transforms and Hankel matrix lifting, allows for the conversion of received signals with interference from 1D to 2D signals. This approach utilizes robust principal component analysis and robust orthonormal subspace learning to decompose target signals from interference, effectively mitigating radar interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple radar sensors operate in the same frequency band to improve detection coverage and sensing capability, then the sensing and detection performance is improved, but interference between radars increases leading to ghost targets and increased noise floors
Solution Approach 1:
The patent extracts and separates the interference signal from the target signal by transforming the received signal into a 2D time-frequency representation using STFT. The low-rank matrix captures the target signal while the sparse matrix captures the interference, allowing selective removal of interference components while preserving target detection capability.
Solution Approach 2:
The patent transforms the 1D received signal into a 2D matrix representation through short-time Fourier transform (STFT). This dimensional transformation enables the separation of target and interference signals by exploiting their different structures in the time-frequency domain, where target signals exhibit low-rank properties and interference exhibits sparsity.
2Measurement precision
If conventional signal processing methods are used to detect targets, then the processing is simple and fast, but target signals are suppressed by interference and detection accuracy decreases
Solution Approach 1:
The patent applies STFT to transform the 1D signal into a 2D time-frequency matrix, enabling the use of low-rank matrix decomposition techniques. This dimensional transformation allows the separation of target and interference signals based on their different structural properties in the time-frequency domain, significantly improving detection accuracy despite increased processing complexity.
Solution Approach 2:
The patent changes the representation parameters of the signal by transforming it from the time domain to the time-frequency domain using STFT. This parameter transformation reveals the low-rank and sparse structures of different signal components, enabling effective separation and interference mitigation while improving target detection precision.
Data Source
AI summary
A method of processing radar data includes detecting a received signal including one or more targets signals and interference, converting the received signal to a matrix signal, and decomposing the matrix signal into a low-rank matrix and a sparse matrix using a group sparsity based low-rank and sparse decomposition method. The method also includes computing an inverse transform of the low-rank matrix and outputting the one or more target signals.