Joint Multiple-Chirp Processing for Radar Velocity Resolution
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Solution Overview
Problem
Radar systems in automated driving assistance face challenges in improving both maximum unambiguous range and maximum unambiguous velocity due to the use of separate FFTs for individual chirp sequences, leading to incoherency and signal gain loss, which affects velocity determination accuracy.
Innovation Solution
The system processes multiple chirp sequences coherently by generating Hankel matrices and performing joint diagonalization of phase shifts across sequences, resolving ambiguities through Doppler division multiplexing and integer unfolding, thereby utilizing all available radar data samples for accurate velocity estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate FFTs are performed on individual chirp sequences to determine velocity, then velocity determination can be achieved, but signal gain is lost and incoherency occurs
Solution Approach 1:
The patent merges multiple separate FFT processing operations into a single joint FFT operation that processes multiple chirp sequences simultaneously. This combining approach maintains coherence across all chirp sequences, preventing signal gain loss while achieving accurate velocity determination through unified spectral analysis.
Solution Approach 2:
The joint FFT operation serves multiple functions simultaneously: it processes all chirp sequences, resolves ambiguities, and maintains coherence in a single operation. This multi-functional approach eliminates the need for separate processing steps that would cause incoherency and signal loss.
2Measurement precision
If multiple chirp sequences are processed separately with FFTs, then velocity can be determined from each sequence, but ambiguities arise that require complex resolution
Solution Approach 1:
The patent combines multiple chirp sequences into a single joint FFT operation, which simultaneously resolves ambiguities across all sequences rather than requiring separate ambiguity resolution for each sequence. This merging approach reduces the overall complexity by handling multiple sequences in one unified processing step.
3Ease of operation
If separate FFTs are applied to each chirp sequence, then processing can be performed on individual sequences, but not all available samples are utilized
Solution Approach 1:
The joint FFT operation merges all available samples from multiple chirp sequences into a single processing operation, ensuring that every sample is utilized in the final velocity determination. This approach eliminates information loss that would occur when processing sequences separately and discarding unused samples.
Data Source
AI summary
A non-transitory computer-readable medium stores machine instructions that cause a processor to obtain a range-sample-antenna data cube for a received radar signal comprising reflections of a plurality of interleaved chirp sequences. The processor generates, for each chirp sequence, a Hankel matrix based on subset of range bins for the particular chirp sequence, and generates a block Hankel matrix based on the Hankel matrices for the plurality of interleaved chirp sequences. The processor performs truncated singular value decomposition to estimate subspaces, and object detection to identify selection matrices. The processor calculates least-squares approximations for the selection matrices and the block Hankel matrix to obtain, for each chirp sequence, a first phase shift matrix θ11 and a second set of phase shift matrices θ1l, l=2, . . . , L. The processor performs joint diagonalization and Doppler division multiplexing compensation on θ11 and θ1l, l=2, . . . , L, then resolves ambiguities in determined velocities based on comparison of θ11 and θ1l, l=2, . . . , L.