Design and Modeling CFAR Algorithms Detecting Target on a Curvilinear Trajectory
Felix Yanovsky (National Aviation University, TU Delft - Atmospheric Remote Sensing)
Igor Prokopenko (National Aviation University)
Alexander Pitertsev (National Aviation University)
Huinam Rhee (Sunchon National University)
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Abstract
This study explores the design and modeling of Constant False Alarm Rate (CFAR) algorithms for detecting targets along curvilinear trajectories in cluttered environments. By focusing solely on primary signal processing, the research introduces a robust approach tailored for nonlinear target motion without post-detection filtering. Using a generalized radar signal processing model and leveraging advanced statistical simulations, the performance of traditional, ordinal, and locally optimal rank-based CFAR algorithms is evaluated. The findings highlight the efficacy of rank-based algorithms under complex clutter conditions, offering significant improvements in detection accuracy and operational reliability.
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File under embargo until 17-05-2026