Radar Signal Processing Circuitry for Efficient Noise Floor Estimation
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Solution Overview
Problem
Radar systems face inefficiencies in processing signals due to excessive computational expense in estimating noise floors, particularly with current digital-signal-processors that support wide SIMD instructions, leading to challenges in differentiating targets from noise.
Innovation Solution
The implementation of circuitry that assesses and differentiates data using selection and threshold-setting circuits, allowing for efficient estimation of noise floors by updating bins with cumulative sums and refining thresholds, enabling simultaneous processing of multiple data elements and reducing computational efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a histogram computation is used to estimate noise floor with CFAR algorithm, then target detection accuracy is improved, but computational expense and processing time become excessive
Solution Approach 1:
The patent segments the noise floor estimation process by dividing the range-Doppler map into multiple cells and processing them independently. Each cell is processed through simplified thresholding operations rather than full histogram computations, reducing overall computational complexity while maintaining detection accuracy across the entire signal space.
Solution Approach 2:
The patent applies partial action by using simplified threshold-based processing instead of complete histogram computations for noise floor estimation. The CFAR algorithm is applied selectively to cells that require it, rather than computing full histograms for all cells, thereby reducing computational expense while maintaining sufficient accuracy for target detection.
2Reliability
If CFAR algorithm with histogram computation is applied, then false alarm rate is reduced, but computational complexity increases significantly
Solution Approach 1:
The patent segments the radar signal processing into independent cells across the range-Doppler map, applying simplified CFAR-like thresholding to each cell separately. This segmentation allows the system to maintain reliable false alarm control through statistical thresholding while avoiding the high computational complexity of global histogram computations.
Solution Approach 2:
The patent substitutes the mechanical/computational intensive histogram computation process with a more efficient threshold-based detection mechanism. By replacing full histogram calculations with simplified threshold comparisons and statistical moment calculations, the system maintains CFAR reliability while significantly reducing computational complexity.
3Productivity
If wide SIMD instructions are used for processing, then processing throughput is improved, but computational expense for histogram computations remains excessive
Solution Approach 1:
The patent applies partial action by using SIMD instructions for only the essential threshold comparison and cumulative sum operations rather than attempting to vectorize the complete histogram computation process. This selective application of SIMD optimization reduces energy consumption while maintaining the throughput benefits of parallel processing for the critical detection operations.
Data Source
AI summary
Exemplary aspects are directed to circuitry that assesses and differentiates a set of targeted data and updates a high-level bin with a numerical value indicating the number of data elements that compared successfully with a predefined value range defined for each bin. A cumulative sum of the high-level bins may then be calculated. Following, a target threshold may be compared to the cumulative sum at each bin and then providing an indication upon discovering a cumulative sum exceeding the threshold. The targeted data may be further refined by changing (through circuitry or other intervention) the predefined range values and then reprocessing the targeted data.