Multi-Aircraft Formation Recognition Method of Over-the-Horizon Radar Based on Deep Transfer Learning

Abstract

Over-the-horizon radar (OTHR) is an important equipment for the ultralong-range early warning in the military, but the use of constant false-alarm rate (CFAR), which is a traditional detection method, makes it difficult in multiaircraft formation recognition. To solve this problem, a multi-aircraft formation recognition method based on deep transfer learning in OTHR is proposed. First, the range-Doppler images of aircraft formation in OTHR are simulated, which are composed of four categories of samples. Secondly, a recognition model based on Convolutional Neural Network (CNN) and CFAR detection technology is constructed, whose training method is designed as a two-step transfer. Finally, the trained model can well distinguish the spectral characteristics of aircraft formation, and then recognize the aircraft number of a formation. Experiments show that the proposed method is better than the traditional CFAR detection method, and can detect the number of aircraft more accurately in the formation with the same false alarm rate. INDEX TERMS multi-aircraft formation; range-Doppler image; OTHR; deep transfer learning

Ship Formation Identification with Spatial Features and Deep Learning for HFSWR

“…The experiments in a clean background have proven the effectiveness of the proposed method. Liang et al [26] simulated the over-thehorizon radar (OTHR) echo images of MAF with multipath effect using spectral color blocks number and amplitude. They adopted a convolutional neural network (CNN) to recognize the number of aircraft and conducted experiments in homogeneous clutter.…”

“…The proposed CNN-ELM is compared with the classical Alexnet and the Resnet18 used in [26]. The Alexnet consists of five convolution layers and three fully connected layers, with activation functions for ReLU and Dropout layers to enhance the model generalization ability.…”