Radar working mode recognition based on hierarchical feature representation and clustering
Yun Ma, Yanbing Li, Meilu Zhu, Jun Wei Zhang · IET conference proceedings. · 2021
The classification of intercepted radar signals has gained considerable attention in the field of electronic reconnaissance. Currently, the multi-function radar (MFR) is capable of transmitting complex and agile signals with different working modes, and the classification of radar waveforms using the traditional methods does not provide satisfactory results. Therefore, it is urgent to develop a new intelligent algorithm to recognize the working mode of MFR. In particular, this paper designs a novel feature extraction method to obtain the sequential relationship of the input signals. At the same time, a hierarchy of signal features is established to represent the signals layer by layer, and then the working mode of the emitter is determined effectively by unsupervised clustering method. The effectiveness and robustness of the proposed method is experimentally verified, through simulating the real complex electromagnetic environment and generating the signal samples of MFR.