Knowledge-Aided Hybrid CFAR Radar Detection
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Solution Overview
Problem
High Frequency Surface Wave Radar (HFSWR) systems face challenges in maintaining a constant false alarm rate due to varying noise levels in different conditions and locations, which affects target detection accuracy and reliability.
Innovation Solution
The implementation of hybrid CFAR detection methods that incorporate knowledge of the local noise environment, allowing for adaptive threshold adjustments and selection of optimal detection methods based on the specific noise conditions, such as ionospheric clutter and Bragg lines, to enhance detection sensitivity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional CFAR detection methods are used, then the detection process is simple, but the false alarm rate cannot be maintained constant due to varying noise levels
Solution Approach 1:
The patent implements dynamic adaptation of CFAR detection methods based on real-time noise environment classification. The system automatically selects between different CFAR variants (CA-CFAR, OS-CFAR, SO-CFAR, GO-CFAR) depending on whether the environment is homogeneous or non-homogeneous, allowing the detection method to dynamically adjust to maintaining constant false alarm rate despite varying noise conditions
Solution Approach 2:
The system changes the detection parameters by selecting different CFAR methods based on noise environment characteristics. When homogeneous noise is detected, CA-CFAR is used; when non-homogeneous noise is detected, OS-CFAR or SO-CFAR is used. This parameter change strategy allows the system to adapt to varying noise levels while maintaining reliable detection performance
2Measurement precision
If adaptive threshold adjustments are made based on local noise environment, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The system performs preliminary classification of the noise environment before applying the detection method. By first determining whether the environment is homogeneous or non-homogeneous and identifying the type of noise (ionospheric clutter, Bragg lines, etc.), the system can pre-select the appropriate CFAR method, avoiding the need to compute multiple detection methods simultaneously and reducing overall computational complexity
Solution Approach 2:
The detection process is segmented into distinct stages: noise environment classification, CFAR method selection, and target detection. This segmentation allows the system to handle complexity systematically by dividing the task into manageable parts, where each stage processes specific information and passes results to the next stage, improving overall efficiency
3Reliability
If the reference window size is increased to improve detection reliability, then detection reliability improves, but the response time increases
Solution Approach 1:
The system applies different reference window sizes based on the local noise environment characteristics. In homogeneous environments, a larger reference window is used to improve reliability. In non-homogeneous environments with high clutter, a smaller reference window is used to reduce processing time and avoid including irrelevant clutter data, thus balancing reliability and response time requirements
Data Source
AI summary
Various embodiments are described herein for a detector and method that perform various types of CFAR detection on radar data including knowledge-aided CFAR detection, hybrid-CFAR detection and simplified censored CFAR detection. Knowledge about the type of local environment of a Cell Under Test and the proximity of the Cell Under Test to various types of noise can be used to select particular types of CFAR detection methods or combinations thereof. In other instances, certain parameters of a CFAR detection method can be adapted based on this knowledge.