Radar Spectrum Reconstruction Using Binary Mask Matrix
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Solution Overview
Problem
Current methods for reconstructing two-dimensional range-Doppler spectra from radar sensor signals disrupted by interference are computationally complex and power-intensive, limiting their application in vehicle radar systems.
Innovation Solution
A method that filters and samples radar signals to generate a discrete beat signal, detects disrupted sampling values, and uses a binary mask matrix to reconstruct the spectrum efficiently through compressed sensing, allowing for direct 2D processing and reduced computational complexity by fixing the size of the transformation matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If methods for compressed sensing are used to reconstruct the spectrum from disrupted radar signals, then the reliability of spectrum reconstruction is improved, but the computing complexity and computing time increase significantly
Solution Approach 1:
The patent segments the beat signal into discrete sampling values and identifies disrupted values individually, allowing selective reconstruction only from valid samples. This segmentation enables the system to maintain reliability by excluding corrupted data while reducing computational load by processing only necessary portions of the signal.
Solution Approach 2:
The patent applies partial compressed sensing by reconstructing the spectrum using only the undisrupted sampling values rather than attempting to process all samples. This partial action approach maintains reconstruction reliability while significantly reducing computing complexity compared to processing the entire signal matrix.
2Reliability
If methods for compressed sensing are used to reconstruct the spectrum from disrupted radar signals, then the reliability of spectrum reconstruction is improved, but the computing time increases significantly
Solution Approach 1:
The patent performs preliminary detection and identification of disrupted sampling values before the reconstruction process. By pre-characterizing which samples are valid and which are corrupted, the system can skip unnecessary computational operations on disrupted values, thereby reducing overall computing time while maintaining reconstruction reliability.
Solution Approach 2:
The patent reduces computing time by applying partial compressed sensing that processes only the subset of undisrupted sampling values. This selective processing approach eliminates wasteful computation on corrupted data while preserving the reliability of the final spectrum reconstruction.
3Reliability
If direct application of compressed sensing methods is used in radar systems for vehicles, then the reliability of interference mitigation is improved, but the device complexity and power requirements make it suitable only for limited applications
Solution Approach 1:
The patent applies local quality by treating different sampling values differently based on their quality status. Valid samples are processed through the reconstruction algorithm, while disrupted samples are identified and excluded. This localized differentiation maintains reconstruction reliability while reducing the overall computational burden compared to uniform processing of all samples.
Solution Approach 2:
The patent implements partial compressed sensing that applies the full reconstruction algorithm only to the subset of valid sampling values. This partial application maintains the reliability benefits of compressed sensing for interference mitigation while significantly reducing the computing power requirements and device complexity needed for implementation in vehicle radar systems.
Data Source
AI summary
A method for reconstructing a two-dimensional range-Doppler spectrum from a signal of a radar sensor disrupted by interference for a vehicle. A transmission signal is emitted and a received signal is received that correlates to the transmission signal. The received signal is filtered and sampled. A discrete beat signal is determined from the filtered and sampled received signal. Disrupted sampling values are detected in the discrete beat signal. A binary mask matrix is generated for marking disruption-free sampling values and for masking disrupted sampling values in the discrete beat signal. The spectrum is reconstructed from disruption-free sampling values of the discrete beat signal with the aid of a transmission function. Remaining value updates are monitored during the reconstruction of the spectrum with the aid of the mask matrix.