Convolutional Neural Network-Based Target Detector Using Maxpooling and Hadamard Division Layers in FM-Band Passive Coherent Location

The constant false alarm rate (CFAR) has been widely used in radar systems to detect target echo signals because of its simplicity. With the recent development of different types of neural networks (NNs), NN architecture-based target detection methods are also being considered. Several studies related to NN-based target detectors have introduced multi-layer perceptron-based and convolutional neural network (CNN)-based structures. In this paper, we propose a CNN-based target detection method in frequency modulation (FM)-band passive coherent location (PCL). We improved the detection performance using a maxpooling layer and a Hadamard division layer, which are parallelly placed with a CNN layer. Moreover, in our method there is no need to determine the specific cell configuration (e.g., cell under test, reference cells, and guard cells) because the proposed method obtains the trained kernels by end-to-end learning. We show that the trained kernels help in the extraction of either signal or noise components. Through the simulations, we also prove that the proposed method can yield an improved receiver operating characteristic compared to that of a cell-averaging CFAR detector for FM-band PCL in a homogeneous environment.