Continuous Phase Modulation (CPM) signal has been widely used in modern satellite, mobile communication and
military communication system due to its good spectrum and power utilization and constant envelope characteristics.
The premise of demodulation or jamming of CPM signal intercepted in military communication confrontation is the
accurate estimation of signal parameters. Aiming at the difficulty and complexity of multi-h CPM signal modulation
index estimation, this paper puts forward a kind of blind estimation algorithm of the modulation index based on first order
cyclic moment and the second-order cyclic cumulants, using the first and second order cyclic properties in
frequency domain on the spectral properties to signal parameter estimation. The estimation algorithm is suitable for both
single h CPM signals and multi-h CPM signals, where the multi-h CPM requires the synchronization of the guidance
sequence. Simulation results show that the algorithm has better performance under low signal-to-noise ratio with less
symbols required. When the number of symbols is 128 and the signal-to-noise ratio is 15 dB, the accurate recognition
rate of the multi-index CPM signal can reach 98% and the recognition rate can reach 99% with the allowable error of
1/32.
Cognitive communication countermeasure system utilizes artificial intelligence technology to quickly realize electromagnetic dynamic perception and electronic jamming strategy generation. In the complex electromagnetic environment of the modern battlefield, continuous phase modulation (CPM) signals are getting more and more attention due to high spectral efficiency and power efficiency. CPM signal denoising processing helps to improve electromagnetic dynamic perception performance. In this paper, a novel model, namely attentional denoising autoencoder (ADE), is proposed with enhanced signal denoising by introducing self-attentional mechanism into the autoencoder. The proposed method divides the one-dimensional communication signal sequence into fixed-size signal patches satisfying the same modulation law, and then utilizes the parallel computing of the self-attention mechanism to model the dependencies between the signal patches, and finally average pooling is used to synthesize the information of each signal patch to reconstruct the signal. The simulation results demonstrate that the proposed model is superior to other methods in terms of the denoising effect, and has a high degree of waveform recovery, which is helpful for the subsequent perception and processing of CPM signals.
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