Research on Target Detection Algorithm for Complex Traffic Scenes Based on ADVI-CFAR

To address the issue of reduced target detection accuracy due to interfering targets and clutter reference cells in complex traffic scenarios, we propose the ADVI-CFAR (Adaptive Discriminant Variation Index Constant False Alarm Rate) detection algorithm. Considering that the non-uniformity of the background environment leads to significant variations in signal power magnitude, we introduce a background power transition point to evaluate the uniformity of the background environment within the reference window. Moreover, in complex background environments, clutter distributions often exhibit skewness rather than a Gaussian distribution. We incorporate the higher-order statistical skewness of the clutter to calculate the background power threshold index, thereby improving the accuracy of background power estimation. Then, based on the transition points and clutter power index, the background environment is classified, and an appropriate detection threshold calculation method is chosen for target detection. We conduct a simulation analysis in uniform, non-uniform, and clutter edge environments, and the results show that the identification accuracy exceeds 95% for all three background environments. At a detection probability of 50%, the performance loss is 0.08 dB in uniform environments and 0.36 dB in multi-target environments. When the false alarm probability is set to 10−4, the ADVI-CFAR algorithm significantly suppresses false alarms, with the false alarm peak occurring at 10−3.52. Real data from urban traffic scenarios validate the method, showing that it achieves a high detection accuracy for target detection in real traffic scenarios and effectively meets the radar target detection requirements in practical traffic environments.