Clutter Map Constant False Alarm Rate Mixed with the Gabor Transform for Target Detection via Monte Carlo Simulation

• Published in: Applied sciences Vol. 14; no. 7; p. 2967
• Main Authors: Mbouombouo Mboungam, Abdel Hamid, Zhi, Yongfeng, Fonzeu Monguen, Cedric Karel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2024
• Subjects: Algorithms; cluttered environments; detection probability; Detectors; false alarm rate; False alarms; Hypotheses; Methods; Monte Carlo method; Monte Carlo simulation; Optimization techniques; Probability; Radar systems; Sensors; Signal processing; Simulation methods; Systems stability; Technology application; time-frequency analysis tools
• DOI: 10.3390/app14072967
• ISSN: 2076-3417

Radar detection is a technology frequently used to detect objects and measure the range, angle, or velocity of those objects. Several studies have been performed to improve the accuracy and performance of detection methods, but they encountered a strong challenge, which was the minimization of false alarms and the distinguishing of real targets from false alarms, especially in nonhomogeneous environments. We propose a new detection method that uses time-frequency analysis tools to improve detection performance and maintain a low constant false alarm rate. Different from existing works, this paper combines the clutter map constant false alarm rate technique with the Gabor transform for accurate target detection in cluttered environments. We suggest the combination of a CFAR detector with a time-frequency method that enables us to tackle challenging scenarios involving near targets. The proposed method allows for locating the exact position of the target by reducing the impact of clutter and maintaining a low rate of false alarms, while the Gabor transform facilitates the extraction of pertinent target characteristics and improves differentiation from clutter. Through experiments and simulations in different scenarios and clutter models, we demonstrate that the method is efficient in measurements and performs well in cluttered environments. This research has a major impact on signal processing and significantly improves target detection in cluttered environments, allowing this method to be deeply developed and implemented.