Automotive Radar Sparse Array DOA Estimation with FFT Processing
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Solution Overview
Problem
Conventional BLRC-based DOA estimation algorithms for automotive radar systems require substantial memory and computing power, limiting their applicability to small-sized arrays and incompatible with modern vehicle radar systems that demand high angular resolution.
Innovation Solution
A novel implementation of the BLRC algorithm, referred to as fastBLRC, reduces memory and computer requirements by utilizing Fast Fourier Transform (FFT) equivalent operations, requiring only (N^3 + M log M) computation and (N^2 + M) memory, where N is the number of virtual antennas, significantly less than the conventional M^3 and M^2 requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional BLRC-based DOA estimation algorithms are used, then measurement precision is improved, but device complexity increases due to substantial memory and computing power requirements
Solution Approach 1:
The patent transforms the conventional BLRC algorithm by changing the computational parameters from direct matrix operations (O(M^3)) to FFT-based operations (O(M log M)). This parameter transformation maintains the super-resolution DOA estimation capability while dramatically reducing the computational complexity and memory requirements, making the algorithm feasible for real-time automotive radar applications.
Solution Approach 2:
The patent substitutes the conventional matrix computation mechanism with an FFT-based computational mechanism. By replacing direct matrix multiplication and inversion operations with FFT equivalent operations, the system achieves the same DOA estimation function with significantly reduced computational burden, enabling implementation in resource-constrained automotive radar environments.
2Measurement precision
If conventional BLRC algorithm is implemented, then DOA estimation accuracy is improved, but productivity decreases due to high computational overhead
Solution Approach 1:
The patent changes the computational parameters from conventional matrix operations requiring O(M^3) computations to FFT-based operations requiring only O(M log M) computations. This parameter transformation maintains super-resolution accuracy while improving processing speed by several orders of magnitude, enabling real-time DOA estimation in automotive radar systems.
3Measurement precision
If array size is increased to improve angular resolution, then measurement precision is improved, but device complexity increases due to quadratic growth in memory requirements
Solution Approach 1:
The patent transforms the memory complexity parameter from quadratic O(M^2) in conventional BLRC to linear O(M) in the FFT-based implementation. This parameter change allows the system to scale to larger array sizes and higher angular resolutions without the memory requirements growing quadratically, making large-array imaging radar systems feasible.
Data Source
AI summary
A system and method for processing received radar signals is presented. A steering matrix for the radar system is determined. A measurement vector is determined using signals received by a plurality of receiver modules. An output amplitude vector is initialized using the steering matrix and the measurement vector. An optimized output amplitude vector is determined by determining a diagonal loading vector using the output amplitude vector, calculating a weighting matrix using a first fast Fourier transform of the diagonal loading vector, determining an inverse matrix using the weighting matrix and an identity matrix, and executing a second fast Fourier transform using the inverse matrix to determine the optimized output amplitude vector, wherein the optimized output amplitude vector is determined by a relationship between the steering matrix and the measurement vector. An objects direction of arrival is determined by correlating the optimized output amplitude vector to the measurement vector.