Radar Sensor Coefficient Module Memory Optimization
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Solution Overview
Problem
Existing radar sensors for motor vehicles face challenges in efficiently processing distance/velocity radar images due to the need for large memory spaces to store coefficients for transform functions, which hinders real-time processing.
Innovation Solution
The radar sensor employs a coefficient module with a memory that stores an initial set of fewer than N coefficients and a recursion module for calculating the remaining coefficients recursively, allowing for fast coefficient calculation and reduced memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of coefficients are stored in memory for the transform function, then the processing accuracy and completeness is improved, but the memory space requirement increases significantly
Solution Approach 1:
The patent pre-calculates and stores only the initial set of coefficients (c(0,k)) for k=0 to Nslow-1 in memory, rather than storing all Nfast×Nslow coefficients. This preliminary action of pre-computing and selectively storing reduces memory requirements while maintaining processing accuracy through the recursion relationship that generates remaining coefficients on-demand.
Solution Approach 2:
The patent extracts only the essential initial coefficients from the complete coefficient set and stores them in memory, while deriving the remaining coefficients through recursive calculation. This extraction of critical components reduces the quantity of stored data while preserving the functional integrity of the transform operation.
2Reliability
If all coefficients are calculated and stored in advance, then the processing completeness is improved, but the memory space and processing time increase
Solution Approach 1:
The patent performs preliminary calculation and storage of only the initial coefficients c(0,k), which form the foundation for generating all other coefficients through recursion. This selective preliminary action ensures processing completeness for the transform function while minimizing the time and resources required for preparation.
Solution Approach 2:
The patent transitions from a static approach of pre-storing all coefficients to a dynamic approach where coefficients are generated on-demand through recursion. The coefficient module calculates coefficients c(n,k) dynamically during processing based on the recursion relationship, allowing the system to adapt coefficient generation to actual processing needs rather than requiring all coefficients to be pre-computed and stored.
3Quantity of substance
If coefficients are calculated online during processing, then memory space is reduced, but the processing speed decreases
Solution Approach 1:
The patent implements a dynamic coefficient generation system where coefficients are calculated on-demand through recursion rather than being statically pre-stored or purely computed online. The recursion module generates coefficients c(n,k) dynamically during processing based on the relationship with previously calculated coefficients, optimizing the balance between memory usage and processing speed.
Solution Approach 2:
The patent performs preliminary calculation of the initial coefficients c(0,k) and stores them in memory, providing a foundation that accelerates subsequent coefficient generation through recursion. This preliminary preparation reduces the computational burden during online processing compared to calculating all coefficients from scratch, thereby maintaining processing speed while reducing memory requirements.
Data Source
AI summary
Radar sensor for motor vehicles. The radar sensor has a high-frequency part, which is configured to transmit sequences of modulated radar pulses and to receive the corresponding radar echoes, and an electronic evaluation part, which is configured to take distance and angle measurements using a synthetic aperture, and includes a scanning module, an FFT module for performing fast Fourier transforms to calculating a two-dimensional distance/velocity radar image, a transform module configured to transform the raw data, while simultaneously correcting migration effects, into a format that can be processed by the FFT module, by applying a transform function defined by a number N of coefficients, and a coefficient module for preparing the coefficients for the transform module. The coefficient module including a memory, in which there is stored an initial set of coefficients comprising fewer than N coefficients, and a recursion module, for recursive calculation of the remaining coefficients.