Abstract
It is difficult to recognize Automatic Link Establishment (ALE) behaviors of a short-wave 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 short-wave 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.
Highlights
The Automatic Link Establishment (ALE) behaviors of a short-wave radio station include Call, Handshake, Notification, Time Offset, Group Time Broadcast, Broadcast and Scanning Call
We propose a novel approach for ALE behaviors recognition of a short-wave radio station without the radio station’s communication protocol, which uses DenseNet to automatically extract deep features of ALE signals
The ALE behaviors signals are simulated according to the communication protocol, but the communication protocol is not utilized during in the process of ALE behaviors recognition
Summary
The ALE behaviors of a short-wave radio station include Call, Handshake, Notification, Time Offset, Group Time Broadcast, Broadcast and Scanning Call. The Link Establishment (LE) behaviors recognition of a short-wave 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 short-wave 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]. The purpose of ALE behaviors recognition is achieved by directly analyzing the signals in physical layer, avoiding
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