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In recent years, the rapid development of deep learning technology has brought new opportunities for specific emitter identification and has greatly improved the performance of radar emitter identification. The most specific emitter identification methods, based on deep learning, have focused more on studying network structures and data preprocessing. However, the data selection and utilization have a significant impact on the emitter recognition efficiency, and the method to adaptively determine the two parameters by a specific recognition model has yet to be studied. This paper proposes a knowledge graph-driven convolutional neural network (KG-1D-CNN) to solve this problem. The relationship network between radar data is modeled via the knowledge graph and uses 1D-CNN as the metric kernel to measure these relationships in the knowledge graph construction process. In the recognition process, a precise dataset is constructed based on the knowledge graph according to the task requirement. The network is designed to recognize target emitter individuals from easy to difficult by the precise dataset. In the experiments, most algorithms achieved good recognition results in the high SNR case (10–15 dB), while only the proposed method could achieve more than a 90% recognition rate in the low SNR case (0–5 dB). The experimental results demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Since orthogonal frequency division multiplexing (OFDM) signals are sensitive to Doppler shift, OFDM signals with cyclic prefix (CP) not only reduce the communication rate but also result in symmetric pseudo peaks in the ambiguity function of the radar, which eventually lead to the degradation of the performance of the joint radar communication system (JRCS). In this study, a novel JRCS is proposed based on the filter bank multicarrier (FBMC) waveform for the inverse synthetic aperture radar (ISAR) imaging and communication system. On the one hand, FBMC waveforms are capable of low orthogonality between subcarriers, which can counter Doppler and multipath effects. On the other hand, FBMC waveforms do not require CP, so the false targets can be avoided and spectral efficiency can be improved. The influence of multipath effect and Doppler shift on FBMC waveform in an integrated system is analysed in detail, and a Doppler compensation algorithm suitable for FBMC‐integrated waveform is proposed. The phase error caused by target motion can be iteratively compensated to obtain a well‐focussed ISAR image. Finally, considering the ISAR image entropy after motion compensation as the evaluation metric, extensive simulation experiments verify the noise robustness and effectiveness of the proposed algorithm. Compared with the conventional OFDM and CP‐OFDM schemes, the simulation verifies that the proposed waveform design and processing method has better performance in the ISAR imaging and communication integrated system. [ABSTRACT FROM AUTHOR]