Based on Shil'nikov criterion, i.e., extending the number of unstable saddle-focus-type equilibria of chaotic system, a rich and varied multiwing chaos with complicated topology and remarkable pseudorandomness has been coined. However, it is a challenging task to construct multiwing chaos with only stable node-focus-type equilibria as the Shil'nikov criterion is not applicable in the circumstances. To eliminate this difficulty and enrich the multiwing chaos, we construct a simple three-dimensional autonomous system with only two symmetric stable equilibria, and further introduce a sawtooth wave modulation function to configure more equilibria. The highlight is that multiwing chaotic attractors can be generated from system with two or multiple stable node-foci. This characteristic indicates that the attractors can be hidden oscillating and are difficult to be located in the small neighborhoods of equilibria, which makes the system has good concealment performance for security applications. The design mechanism and system properties are theoretically analyzed and numerically investigated using phase portraits, finite-time Lyapunov exponents, and basins of attraction. Hardware experiments based on field-programmable gate array verify the feasibility of the system. Finally, based on the generated multiwing hidden chaotic attractors, an image encryption scheme with the high security performance is designed to enhance its application.

Low Probability of Intercept (LPI) radar waveform recognition is not only an important branch of the electronic reconnaissance field, but also an important means to obtain non-cooperative radar information. To solve the problems of LPI radar waveform recognition rate, difficult feature extraction and large number of samples needed, an automatic classification and recognition system based on Choi-Williams distribution (CWD) and depth convolution neural network migration learning is proposed in this paper. First, the system performs CWD time-frequency transform on the LPI radar waveform to obtain a 2-D time-frequency image. Then the system preprocesses the original time-frequency image. In addition, then the system sends the pre-processed image to the pre-training model (Inception-v3 or ResNet-152) of the deep convolution network for feature extraction. Finally, the extracted features are sent to a Support Vector Machine (SVM) classifier to realize offline training and online recognition of radar waveforms. The simulation results show that the overall recognition rate of the eight LPI radar signals (LFM, BPSK, Costas, Frank, and T1–T4) of the ResNet-152-SVM system reaches 97.8%, and the overall recognition rate of the Inception-v3-SVM system reaches 96.2% when the SNR is −2 dB.

The electronic reconnaissance system is the operational guarantee and premise of electronic warfare. It is an important tool for intercepting radar signals and providing intelligence support for sensing the battlefield situation. In this paper, a radar waveform automatic identification system for detecting, tracking and locating low probability interception (LPI) radar is studied. The recognition system can recognize 12 different radar waveform: binary phase shift keying (Barker codes modulation), linear frequency modulation (LFM), Costas codes, polytime codes (T1, T2, T3, and T4), and polyphase codes (comprising Frank, P1, P2, P3 and P4). First, the system performs time–frequency transform on the LPI radar signal to obtain a two-dimensional time–frequency image. Then, the time–frequency image is preprocessed (binarization and size conversion). The preprocessed time–frequency image is then sent to the convolutional neural network (CNN) for training. After the training is completed, the features of the fully connected layer are extracted. Finally, the feature is sent to the tree structure-based machine learning process optimization (TPOT) classifier to realize offline training and online recognition. The experimental results show that the overall recognition rate of the system reaches 94.42% when the signal-to-noise ratio (SNR) is −4 dB.

A brief description of the multipath coherent radio technique installation is presented. Its goal is continuous monitoring of the dynamical processes in the ionosphere caused by space weather variations and the impact of high-energy sources in the Earth–atmosphere–ionosphere–magnetosphere system. The variations of radio-wave characteristics (the Doppler spectra and signal amplitudes) in the HF range and the ionospheric parameters over China during the ionospheric storm of August 26, 2018, are described. An ∼50–100 km ascent in the radio-wave reflection region and its oscillations with an amplitude of ∼30–40 km were observed many times during the storm on all paths. The ascents were followed by descents of the radio-wave reflection region by many tens of kilometers. The ascents and descents of the reflection region were caused by a decrease in the electron concentration by a factor of 1.5–2 and its increase by a few times, respectively. The maximal increase in the electron concentration in the E and F regions reached a factor of 1.5 and 3, respectively. The relative amplitude of oscillations in the electron concentration reached many tens of percent. The amplitude of oscillations of frequency Doppler shift was several times lower on the reference days. The observed oscillations in the frequency Doppler shift were apparently caused by generation of the atmospheric gravity waves and their subsequent propagation to the latitudes of the means of observation. The velocity of wave disturbances was ∼275–480 m/s, whereas their period was ∼60 min. The ionospheric disturbances of August 27, 2018, were insignificant despite the repeated magnetic storm.