Switching CA/OS CFAR using neural network for radar target detection in non-homogeneous environment

This paper presents the switching CA/OS CFAR using neural network for improving the radar target detection in non-homogeneous environment. This method uses one of between CA-CFAR and OS-CFAR as output threshold depends on the nearest value with the output of neural network. The neural network used in this research is the Multi-Layer Perceptron (MLP) consisted of two hidden layers. The input of neural network was as many as 3 consisted of CA and OS CFAR and Cell Under Test (CUT) value. The pattern of those inputs will be classified and recognized by the neural network by the training to calculate the preliminary threshold. That threshold will be compared to CA and OS CFAR to select the best final threshold. The method was examined with three simulated common radar cases including homogeneous background, multi target and clutter wall environment. The experiments show that the proposed method is capable to select properly based on the best performance of both CA and OS CFAR in homogeneous and non-homogeneous environments.