Radar Object Detection with Noise-Gated CFAR Processing
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Solution Overview
Problem
Radar systems face challenges in achieving lower power consumption without compromising detection performance, particularly due to the computational intensity of CFAR algorithms, which are essential for maintaining a constant false alarm rate in varying noise levels.
Innovation Solution
Implementing a CFAR detection method that applies the constant false alarm rate (CFAR) threshold only to radar map bins exceeding a noise threshold, estimated during a reference setting mode when no target is present, thereby reducing the number of calculations and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CFAR detection algorithm is applied to all bins in the radar map, then detection accuracy is maintained, but computational load and power consumption increase significantly
Solution Approach 1:
The patent segments the radar map bins into two subsets: a first subset of bins with amplitudes exceeding a noise threshold and a second subset of bins with amplitudes below the threshold. The CFAR detection algorithm is applied only to the first subset, thereby reducing computational load while maintaining detection accuracy for significant targets.
Solution Approach 2:
Instead of applying the CFAR algorithm to all bins (excessive action), the patent applies it partially only to bins that exceed the noise threshold. This partial action reduces computational requirements while still capturing all potential targets, as targets must have amplitudes above the noise floor to be detectable.
2Measurement precision
If CFAR threshold is determined for all bins, then detection performance is maintained, but processing time increases
Solution Approach 1:
The patent divides the set of all bins into two segments based on amplitude comparison with the noise threshold. By segmenting the data this way, the CFAR algorithm only needs to process the smaller first subset of bins that exceed the threshold, significantly reducing processing time while maintaining detection performance.
Solution Approach 2:
The patent uses partial action by applying CFAR only to bins that are necessary for target detection (those exceeding the noise threshold). This eliminates unnecessary processing of low-amplitude bins that cannot contain targets, thereby reducing overall processing time.
3Measurement precision
If noise threshold is not applied before CFAR, then detection sensitivity is maintained, but false alarms increase in high noise environments
Solution Approach 1:
The patent performs preliminary action by first comparing bin amplitudes to the noise threshold and identifying the first subset of bins that exceed it. This preliminary filtering step removes obviously spurious low-amplitude signals before the CFAR algorithm processes them, reducing false alarms in high noise environments while maintaining sensitivity for legitimate targets.
Data Source
AI summary
In accordance with an embodiment, a method of operating a radar system includes: estimating an overall noise level of a received radar signal; determining a first noise threshold based on the estimated overall noise level; generating a radar map having a plurality of bins from the radar signal; determining a first subset of the plurality of bins having amplitudes that exceed the first noise threshold; determining a constant false alarm rate (CFAR) threshold only for each bin of the first subset of the plurality of bins; and determining that bins of the first subset having amplitudes exceeding the CFAR threshold correspond to one or more detected object.