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.

This paper proposes generalized mathematical model of different passive interferences and develops an effective algorithm of digital signal processing for detection on the background of them. Models of interferences as random process of K-distribution is used with parametrization for the unwanted reflections from atmosphere, land, and sea. Robust algorithm for signal detection on the background of such interferences, in particular in case of non-gaussian distribution, is developed. Its effectiveness is researched and confirmed.

The generalized mathematical and computer model of clutter is developed. It is based on K-distribution and can be used for simulation of sea, atmospheric, and land clutter with different characteristics. Examples of model application for testing rank algorithm for clutter suppression are considered and analyzed.