Robust CFAR Detector Based on CLEAN for Sidelobe Suppression

In response to challenges posed by abnormal interference, such as target sidelobes within the reference window, this paper proposes a Sidelobe Suppression-Constant False Alarm Rate (SS-CFAR) detector. Drawing on the CLEAN concept, the SS-CFAR is designed for target detection in indoor environments with multiple targets using millimeter-wave radar. It employs second-order difference statistics combined with the Monte Carlo algorithm and iterative censoring to separate target signals from background clutter in radar echoes. Additionally, sidelobe suppression for the target is achieved by reconstructing the target's echo information and the CLEAN concept. The simulation and actual experimental results demonstrate that SS-CFAR delivers superior detection performance, obviating the need for prior knowledge of anomalies or manual setting of parameters like the number of reference and guard cells, which traditionally depend on experiential adjustments. Compared to mean class and ordered statistics class CFAR detectors, SS-CFAR transcends reference window constraints. It accurately estimates background clutter, enhancing detection capabilities in multi-target environments. This innovative approach not only showcases superior detection performance and adaptability but also paves the way for the broader application of millimeter-wave radar in complex indoor scenarios.