An improved CFAR algorithm for multiple environmental conditions

Abstract

The primary objective of radar digital signal processing is to detect and identify targets in complicated situations, such as those involving clutter or several closely positioned targets. The constant false alarm rate (CFAR) method is more effective for target recognition and has better control over the false alarm rate. Many studies have been conducted on the design of CFAR, but previous CFAR algorithms have not been effective in all or most environmental fields and target scenarios. In this study, an algorithm called Censored Mean Clutter Map CFAR (CM-CM CFAR) has been developed and tested for various environmental conditions. When compared to a fixed false alarm rate, the suggested CFAR algorithm’s Monte Carlo simulation results demonstrated a high detection probability in a variety of environments. This work designs a real-time CM-CM CFAR processor using field-programmable gate array (FPGA) technologies. Xilinx ARTIX 7 FPGA technology is used to develop and map a scalable parallel framework. Consequently, the implementation required 23,741 LUTs and 1825 FF. It is verified that the complexity and operating speed of the suggested CFAR processor are extremely appropriate for real-time implementation when compared to the results of the previously proposed FPGA implementation.

IfCMD: A Novel Method for Radar Target Detection under Complex Clutter Backgrounds

“…Until now, many efforts and contributions have been devoted to the research and application of CFAR detection schemes. However, the non-homogeneous clutter in realistic environments hinders the effectiveness of these CFAR detectors [11][12][13]. (3) The third kind of method models the clutter in space with a settled probabilistic distribution and estimates the clutter precisely [14,15].…”