Radar Signal Processing FFT Bin Selection
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Solution Overview
Problem
Current radar signal processing systems face delays due to the computational complexity of Constant False Alarm Rejection (CFAR) algorithms, which require significant resources and result in limited real-time capability.
Innovation Solution
Implementing a method and device that utilize a Fast Fourier Transform (FFT) engine and a Bin rejection engine to selectively store and process FFT results, reducing memory requirements and computation time by compressing and filtering out unnecessary bins based on predefined conditions such as vehicle speed and environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CFAR algorithms are used for FFT result analysis, then target detection accuracy is improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent extracts and stores only the relevant FFT result bins that correspond to potential target ranges, separating them from the complete FFT spectrum. This extraction process removes unnecessary data while preserving the information needed for accurate target detection, thereby reducing the computational burden of CFAR algorithms without sacrificing detection accuracy.
Solution Approach 2:
The patent segments the FFT results into multiple bin groups based on range information, storing only those segments that are relevant for current detection needs. By dividing the complete frequency spectrum into manageable segments and selectively storing only necessary portions, the system maintains detection precision while significantly reducing processing time and memory requirements.
2Loss of information
If all FFT results are stored for further processing, then complete signal information is preserved, but memory requirements and processing load increase
Solution Approach 1:
The patent extracts only the essential FFT bins that contain relevant target information based on predefined range criteria. By taking out and storing only these critical bins while discarding redundant data, the system preserves necessary signal information while dramatically reducing memory consumption and subsequent processing loads.
Solution Approach 2:
The patent applies local quality by storing FFT results with different retention strategies for different bin ranges. Critical range bins are preserved with full detail, while non-critical bins are discarded or compressed. This localized differentiation ensures that important signal information is maintained while reducing overall memory requirements through selective data retention.
Data Source
AI summary
An example relates to a method for processing radar signals, wherein said radar signals comprise digitized data received by at least one radar antenna, the method comprising (i) determining FFT results based on the digitized data received; and (ii) storing a first group of the FFT results without a second group of the FFT results.