Knowledge-Based Constant False Alarm Rate Detection - KB-CFAR Detector

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Resource Overview

Knowledge-Based Constant False Alarm Rate Detection - KB-CFAR Detector: Includes simulations for KB-CFAR detection on target-free signals with pure noise, and KB algorithm simulations for target-containing signals in Rayleigh clutter backgrounds. Some programs primarily focus on visualization, plotting signal waveforms and detection threshold comparisons during the KB-CFAR detection process; while other programs calculate detection probability and false alarm probability metrics.

Detailed Documentation

In this paper, we introduce the principles and applications of Knowledge-Based Constant False Alarm Rate detection, specifically the KB-CFAR detector. We conduct simulation experiments including KB-CFAR detection on noise-only signals without targets, and KB algorithm simulations for target-containing signals in Rayleigh clutter environments. To better understand and demonstrate the KB-CFAR detection process, we developed specialized programs for plotting signal waveforms and detection threshold comparison charts using MATLAB's plotting functions. Additionally, we implemented computational programs that calculate detection probability (Pd) and false alarm probability (Pfa) metrics during the KB-CFAR process, incorporating statistical analysis algorithms for performance evaluation. Through these experiments and programs, we can comprehensively analyze and assess the performance and effectiveness of the KB-CFAR detector.

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