Recognizing Automatic Link Establishment Behaviors of a Short-Wave Radio Station by an Improved Unidimensional DenseNet

Abstract

It is difficult to recognize Automatic Link Establishment (ALE) behaviors of a shortwave radio station, if we do not acquire the radio station's communication protocol standard. A method is proposed to recognize different ALE behaviors by using an improved unidimensional DenseNet. In this work, we directly recognize ALE signals in physical layer without the radio station's communication protocol standard. Hence, we can avoid difficulties in demodulation, decryption and so on. Actually, the original DenseNet is used extensively in the field of computer vision, so the original DenseNet is firstly adapted for the unidimensional input. And then, two parallel dense blocks are used in our improved unidimensional DenseNet, which could improve the capability of network to extract ALE signals' deep features. The experimental results show that the proposed method is able to recognize different ALE behaviors of a shortwave radio station. And improved DenseNet has better recognition performance than simple DenseNet. The simple DenseNet only contains one dense block. Finally, the results of comparison experiments also show that some classic networks have worse performance in ALE behaviors recognition, such as LeNet-5, ResNet-34, and DenseNet-121. INDEX TERMS Recognition, unidimensional DenseNet, automatic link establishment, shortwave radio station, electronic countermeasure. I. INTRODUCTION The ALE behaviors of a shortwave radio station include Call, Handshake, Notification, Time Offset, Group Time Broadcast, Broadcast and Scanning Call. The Link Establishment (LE) behaviors recognition of a shortwave radio station is important and difficult in the field of electronic countermeasures. Mastering the ALE behaviors of enemy's radio station can effectively infer the communication intention and working status of the radio station in the wireless communication network. It provides a reference for further inferring the topological structure and tactical position of the shortwave radio station. As a reconnoiter in the field of electronic countermeasures, our goal is to recognize a radio station's ALE behaviors without the radio station's communication protocol (MIL-STD-188-141B) [1]. In this work, the purpose of ALE behaviors recognition is achieved by directly analyzing the signals in physical layer, avoiding The associate editor coordinating the review of this manuscript and approving it for publication was Miguel López-Benítez .

Unidimensional ACGAN Applied to Link Establishment Behaviors Recognition of a Short-Wave Radio Station

“…In order to illustrate the advantages of the unidimensional ACGAN+DenseNet algorithm, the proposed method was compared with Multilayer Perceptron (MLP), Lenet-5 [ 29 , 30 ], and unidimensional DenseNet [ 6 ]. They were all used to recognize different LE behaviors.…”

“…Then real samples and generated fake “real” samples were mixed to form a new training set. The unidimensional DenseNet we proposed in [ 6 ] was able to effectively automatically extract the deep features of LE behavior signals, and finally Softmax classifier was used to realize the recognition of a radio station’s different LE behaviors. The whole process of the algorithm’s recognition did not need the help of the communication protocol, which provided a new idea for electronic reconnaissance.…”

“…Research has been done [ 2 , 3 , 4 , 5 ] on the communication behaviors of radio signals, but they all differ from a short-wave radio station’s LE behavior recognition. The research done in [ 6 ] uses a novel improved unidimensional DenseNet to recognize the LE behaviors of a short-wave radio station, and the whole recognition process does not need the help of communication protocol standard, avoiding complex signal feature transformation. However, when there are only a few samples with labels, the improved unidimensional DenseNet recognition accuracy needs to be further improved.…”

Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet

“…The seven types of LE behavior would be categorized as ALE behavior in this work. In fact, there are five kinds of radio station communication behavior, which means the LE behavior recognition proposed by Wu et al [ 5 ] differs from the communication behaviors studied in this work. There are also other methods; for example, the flow mining (FlowMine) algorithm was proposed to mine the instruction flow and was verified with simulated and real data [ 6 ].…”

“…However, these communication relationships are not our focus. However, Wu et al [ 5 ] proposed a method for radio station link establishment (LE) behavior recognition, and the method can recognize seven kinds of LE behaviors without a communication protocol. The seven types of LE behavior would be categorized as ALE behavior in this work.…”