Due to the increasingly complex electromagnetic environment, the electromagnetic space situation has the characteristics of integrity, dynamics, relevance, visibility, mass, multi-dimensionality and so on. The study on the electromagnetic space situation provides an important theoretical support for improving the comprehensive performance of the frequency system, realizing the spectrum sharing of the mobile communication system and ensuring the spectrum security of major security activities, which has gradually become one of the most dynamic research directions in the spectrum field. The related technologies of electromagnetic space situation research at home and abroad are highlighted in this paper, and the representative work is systematically introduced. The importance and development status of electromagnetic space situation research are summarized, and the challenges in this field in the future are put forward.

Transfer learning technology can use experience information to assist current tasks. It has been widely used in the fields of computer vision and speech recognition, whereas it has not made obvious achievements in the electromagnetic field. The electromagnetic environment changes quickly, and the performance of the source data or the classifier model in the new environment will be significantly degraded. Retraining not only requires a lot of data but also takes time and effort. Transfer learning technology is very related to the task of electromagnetic target recognition. Based on the measured electromagnetic target data set, this paper explores several applications of transfer learning in solving the problem of small samples of electromagnetic targets, including the transfer of similar targets and the transfer of heterogeneous targets. Experimental results show that by migrating the pre-training model to the target domain small sample recognition task, when the target domain is a similar source and there are only 20 labeled samples, the verification accuracy is increased by 25% compared with the non-transfer model and the training time is greatly shortened; when the target domain is a heterogeneous source, the training time can be less than 1/5 that of the source domain while ensuring the recognition accuracy.

In complex communication environment, the connection between the characteristics of different signals is seldom considered in modulation recognition. A Convolutional Neural Network(CNN) is built to extract the characteristics of the time-frequency images of signals. Time-frequency transform is employed to process the one-dimensional signal into images, and image features are extracted through CNN. In order to improve the classification and recognition accuracy of the algorithm under low SNR, the texture features are also extracted from the images, and they are fused with the features extracted from the CNN. The simulation results show that the Time–Frequency Convolution Neural Network(TF–CNN) and TF–Resnet framework can achieve signal automatic modulation recognition and classification.

A passive location method to long baseline antenna array is proposed. In this method, an array antenna is embedded into a long baseline system with tri-antenna firstly. According to the principle that the angle between the target and the long baseline is implied in the phase difference information, the angle changing rate can be estimated from the phase difference of the angle between the target and the long baseline. The actual direction of the target can be measured by the array antenna. The position parameters of the target are obtained thereafter. The method can be applied to locate the target by using only a single pulse. The simulation results verify the validity of the proposed method.

Multiple heterogeneous spectrum users require different Quality of Service(QoS) in cognitive radio networks. A dynamic spectrum allocation method is proposed based on multi-agent reinforcement learning. In order to improve the satisfaction of spectrum users, the proposed method is evaluated by the Quality of Experience(QoE) of spectrum users instead of QoS. Multiple virtual agents are established to simulate spectrum users to learn interactively with environment in a cooperative way, and the optimal spectrum allocation can be obtained by integrating their learning and spectrum decision results. Simulation results show that the proposed method can obtain higher QoE performance of secondary users than those methods based on the traditional reinforcement learning. The probability of collision between spectrum users also can be reduced in the proposed method without any information about the usage rules of primary users and dynamic characteristics of channels.

The realization of abnormal detection and pattern discovery of electromagnetic data is of great value to the judgment and early warning of abnormal behaviors of electromagnetic targets. Different types of electromagnetic data usually exist in the form of time series, with the characteristic of imbalance between normal data and abnormal data. To address these issues, a time series anomaly detection method is proposed based on the spatial-temporal joint attention mechanism. The channel attention mechanism and spatial attention mechanism are combined to enhance the feature representation of the abnormal part of time series data. Experimental results show that the proposed detection algorithm can effectively deal with the difficulty of data imbalance and has strong robustness.

At present, artificial intelligence-based methods have been able to achieve good results in radar emitter recognition task. However, with the development of electronic information technology, there will be more and more unknown emitters whose characteristic distribution and categories are unknown. In the absence of prior knowledge, it is difficult to fully train the artificial intelligence model, which makes most of the existing methods unable to well complete the recognition of unknown radar emitters. This paper proposes a big electromagnetic data solution that can be used for the recognition of unknown radar emitters, and then focuses on the Flink-based fast comparison retrieval and recognition algorithm for unknown radar emitters. Finally, a comparative experiment proves the effectiveness of the proposed method, and its recognition accuracy can reach 87.2%. When the parallelism is set to 6, the entire Mutual Information- K-Nearest Neighbor(MI-KNN) parallelization algorithm takes only 4.7 s.

An individual identification method of communication radiation sources based on Power Spectral Density(PSD) fingerprint characteristics and intelligent classifier is proposed in order to prevent the occurrence of problems such as device cloning, replay attacks and user identity impersonation, and to accurately identify and authenticate Internet of Things(IoT) objects. First, the radio frequency baseband signal is collected by receiver, and the in-phase signal is collected. Then the steady-state signal segment is intercepted through variance trajectory detection, and data normalization processing on the steady-state signal segment is performed; the PSD of the steady-state signal segment is calculated after data normalization processing to obtain a feature vector, and the feature vector is used as the radio frequency fingerprint of the transmitter. Finally, an intelligent classifier is adopted to identify the radio frequency fingerprint to complete the individual identification of the communication radiation source. The experimental test to identify eight wireless data transmission radio E90-DTU devices and 100 WiFi network card devices of the same manufacturer, the same type and the same batch shows that the proposed method can obtain good recognition accuracy when applied in Line-Of-Sight(LOS) scenarios, mixed scenes of LOS and Non-line-Of-Sight(NOS) scenarios, low signal-to-noise ratio scenes, and scenarios with a large number of IoT devices, etc.

Radio Frequency Fingerprint(RFF) originates from the differences in transmitter circuit design and the manufacturing tolerance of the hardware circuit in the production process. It is an emerging equipment identification and authentication technology. Modeling the generation mechanism of RFF is the basis for its in-depth research. Based on a general Zero Intermediate Frequency(ZIF) digital communication transmitter architecture, the influence of each component in the transmitter on RFF is analyzed, and the corresponding RFF time-domain baseband model is established as well. In addition, several important time-domain parameter tolerances of communication standards are summarized. The maximum Root Mean Square Error Vector Magnitudes(RMS EVMs) of the two typical modulation methods, Quadrature Phase Shift Keying(QPSK) and 16 Quadrature Amplitude Modulation(16-QAM), are mainly studied under the LTE standard. Finally, through theoretical derivation and Matlab simulation, the upper and lower bounds of Direct Current(DC) offset, In-Phase/Quadrature(I/Q) gain imbalance, I/Q quadrature offset error, I/Q filter offset, oscillator phase noise, and power amplifier nonlinearity parameters are given. The changes of the constellation diagram under the critical conditions of various RFF parameters are also analyzed, which provides reasonable parameter guidance for the future research of RFF extraction and identification.

An intelligent modulation recognition method based on the Convolutional Neural Network(CNN) and two-dimensional Red-Green-Blue(RGB) cyclic spectrum images is proposed in order to improve the modulation recognition accuracy and reduce the computational complexity. The cyclic spectrum can be employed to identify the modulation type. The three-dimensional cyclic spectra are converted to two-dimensional RGB cyclic spectra to reduce the computational complexity, which are then taken to build the data set. Moreover, a CNN based modulation classifier with low computational complexity is proposed. Simulation results show that the proposed intelligent modulation recognition algorithm can achieve higher classification accuracy with lower computational complexity.

Power Spectral Density(PSD) prediction is an important part of spectrum management. Due to the high complexity, nonlinearity and uncertainty of the PSD, it is difficult for a single prediction model to ensure the accuracy and efficiency of the prediction. In order to overcome the disadvantages of a single prediction method, a hybrid machine learning model is proposed to combine a Self-Organizing Map(SOM) network with a Regression Tree(RT) to predict the PSD of the signal. First, the method uses a self-organizing map network to cluster the original sample sets with similar manual features. Then, a RT is constructed for each cluster to predict the PSD. Finally, the data of RWTH from Aachen University are adopted for experiments. The root mean square error of the prediction result is 0.824 higher than that of the existing method, which proves that the hybrid model has higher prediction accuracy and better generalization ability.

Most of the current direct positioning methods are mainly for narrow-band signals. A wide/narrow band signal Direct Position Determination(DPD) method based on distributed Unmanned Aerial Vehicle(UAV) platform is proposed. Firstly, the received data of multi-UAV platforms are synthesized in frequency domain, and a cost function directly related to the source location is established based on multiple frequency points. Secondly, the monitoring area is gridded to determine the location of the target source. Finally, the multi-UAV movement monitoring is carried out, and the monitoring area is continuously reduced. The final positioning result is obtained by clustering analysis of multiple positioning results. Simulation results show that the localization performance of this method is obviously better than that of traditional localization methods. The processing results of the measured data show that the proposed method has better positioning performance than the improved Time Difference of Arrival(TDOA) positioning method.

This paper proposes a multi-scale Convolutional Neural Network(CNN) online pre-earthquake electromagnetic anomaly detection model which is applied in noisy environment. Based on the powerful feature extraction ability of CNN, cooperating with the characteristics of long-term and short-term ground-space electromagnetic spectrum, the pre-earthquake electromagnetic anomaly detection is performed in multi-dimensional and multi-perspective. At the same time, the adaptive Variational Mode Decomposition(VMD) noise reduction method is introduced to extract the effective information in the observation signal. Combined with online learning strategy, the continuous learning of possible changes of pre-earthquake electromagnetic anomaly mode is realized. The simulation results show that the multi-scale model can maintain high accuracy under low Signal-to-Noise Ratio(SNR), and the online learning strategy can effectively reduce the model update time, which proves the effectiveness of the model.

A terahertz electrically controlled reflective phase shifter based on liquid crystal is proposed as well as its beam scanning array antenna. By using the slotted structure, the problems of uneven orientation, edge effect and increased saturation voltage due to the insufficient coverage of resonant layer in the process of liquid crystal regulation can be solved. A phase shifter with three slots at 380 GHz is designed, and the surface current distribution and resonance frequency points are simulated. The numerical results show that the phase shifter can achieve 360°phase shift in 377-392 GHz when the relative dielectric constant of liquid crystal changes in 2.47-3.26. An electronically controlled beam scanning array antenna operating at 380 GHz is designed by using a reflective phase-shifting unit with three slots. One dimensional beam scanning within 30°is realized, and the main lobe gain is greater than 20 dBi.

This paper studies the enhancement effect and mechanism of surface chemical sulfur-selenium passivation technology on the performance of gallium arsenide-based light-controlled terahertz modulation devices. Experiments show that sulfur-selenium passivation can effectively remove gallium arsenide surface oxides, reduce surface recombination centers, and improve the surface state of gallium arsenide. After passivation, the Photoluminescence(PL) intensity is significantly enhanced, which is 6 times that of the reference gallium arsenide substrate. At the same time, the passivation effect can significantly increase the lifetime of minority carriers in gallium arsenide, up to 2.2 ns. The surface passivation effect can significantly improve the modulation performance of the GaAs-based terahertz modulator. The measured modulation depth is 41% and the modulation rate is 88.81MHz under 3 mW laser power. The optically controlled terahertz modulator has high modulation depth and modulation rate at low power, and has huge application potential in the field of terahertz communication.

Photo-Conductive Antenna(PCA) is a commonly used terahertz transmitting device. How to improve the radiation efficiency of PCA has become the research focus of domestic and foreign researchers. In this paper, the radiation efficiency of the PCA is studied by using the Finite Difference Time Domain(FDTD) method. The columnar structure is added to the surface of the photosensitive layer, so that more light is captured in the photosensitive layer. The simulation results show that the structure can significantly enhance the radiation efficiency. By comparing the enhancement effects of materials and columnar shapes, the results show that the enhancement effects of the PCA of silver material, gold material, and gallium arsenide material sequentially decrease in the same columnar structure; in the same material, the enhancement effects of the PCA with the regular hexagonal prism structure, the cylindrical structure, and the regular quadrangular prism structure decrease sequentially. Through the optimal combination of materials and structures, the highest and the lowest enhancement efficiencies are respectively 1 100% and 150% that of the traditional PCA.

A method of the concealed object segmentation based on maximum two-dimensional entropy for passive terahertz security is proposed. The method firstly employs a filter bank to reduce image noise. A self-generated detection region algorithm is designed, which can automatically cover the key detection area. The concept of two-dimensional entropy is introduced to implement the concealed object segmentation. Evaluation and comparison experiments are conducted in 0.2 THz band passive images, demonstrating that the method has a good segmentation performance and real-time performance. It may have an important application in the automatic detection for terahertz security.

In order to solve the reliability control problem of thousands of microwave modules in the space-borne Synthetic Aperture Radar(SAR) phased array antenna, a design scheme of beam-steering system based on parallel chain topology is proposed. This solution uses a chain topology bus to simplify unit interconnection and realize lightweight design. It proposes a normalization method of the receiving circuit to realize peer-to-peer interchange of the beam-steering unit, and elaborates the reliability design measures to prevent interface circuit failure from the aspects of DC bias, fault isolation, and synchronous transmission. The test results show that when the threshold value of the receiving chip is 400 mV, the DC bias resistance value should be less than 690 . to avoid the output instability at the receiving end; when any receiving end in the bus fails, a 1 k. isolation resistor can be connected in series to prevent the bus communication from being disabled; when the falling edge of the clock is located at 1/2 of the length of the data symbol, the transmission is error-free. The research results can provide a reference for the reliability design of subsequent space-borne large phased array radar beam-steering systems and other similar space loads.

In the field application of WiFi external radiation source radar, the location of the indoor radiation source needs to be acquired in real-time. Through detailed analysis of the reflection mode and characteristics of the signal in the indoor environment, the first reflected signal is adopted to establish the Time Difference Of Arrival(TDOA) model of the WiFi radiation source measurement, and the equation is solved by deriving the coordinates of the radiation source. For this nonlinear equation, Taylor expansion is first employed to linearize it at the initial value; and then Gauss-Newton iteration method is adopted to estimate the radiation source coordinates, which has a faster convergence rate. Simulation analysis shows that the proposed algorithm can realize positioning of indoor radiation source, and the accuracy loss caused by the linearization of the equation can be quickly compensated by iteration.

In view of the disadvantages of the traditional ionospheric oblique sounder like complex structure, the fixed sounding path, and the serious coherent interference among multiple stations, a detection method for the embedded ionospheric oblique sounding system based on the Field Programmable Gate Array(FPGA) and Advanced Reduced Instruction Set Computing Machines(ARM) is proposed, and the design of system structure is put forward. Firstly, the complete complementary code is adopted to solve the problem of coherent interference in simultaneous detection of multiple stations. Secondly, the digital intermediate frequency receiving structure is used for secondary mixing in the digital domain, avoiding spurious signals and intermodulation distortion, bearing the characteristics of standardization, modularization and good expansibility. The Flexible Static Memory Controller(FSMC) protocol provides a high-speed parallel data transmission method between control modules. Finally, the data automatic judgment and optimal frequency selection are realized. The experimental results verify the correctness and reliability of the design. It has important application prospects in space physics, sky-wave over-the-horizon radar detection, emergency rescue and disaster relief and other fields.

Common collector common base cascaded transistor structure can compose limiting amplifiers because of special IV curve between the emitter junctions. The first transistor is common collector and the second transistor is common base so that the amplifier can limit the output wave to non-saturation, which means limiting the output power. Because both of the common collector and common base transistors have broadband structures, the final amplifiers are of broadband characteristic. A broadband limiting amplifier is designed, which operates from 10 MHz to 200 MHz, with the gain of 25 dB, the limiting power of 2 dBm, the return loss upper 20 dB. A compact limiting amplifier is designed by thin film circuit technology, whose size is 7.15 mm×8.1 mm, smaller than that by traditional Printed Circuit Board (PCB) technology. The limiting amplifier has smaller size and more excellent performance.

In the coherent power combing experiment of multi-channel injection-locking of high-power continuous wave magnetron, the analysis on the output character can improve the combing efficiency. An injection-locking system of a 20 kW continuous wave magnetron at S-band is built, which is composed of three small systems: a magnetron signal generating system, an injection locking system and a phase difference detecting system. There are a microwave frequency source and a phase shifter with adjustable amplitude and adjustable frequency in the system. Using the external injection signal, the phase stability, spectrum and phase noise of the output signal of the magnetron are analyzed respectively. The minimum phase difference fluctuation is less than 4°, the maximum is 17°, the locking frequency band varies between 2.9 MHz and 13 MHz, and the phase noise suppression is over 40 dB. The relationships between the injection locking signals and the output signals are also summarized, which lays a foundation for the power combing of magnetrons.

Aiming at the miniaturization design goal of environmental hybrid energy harvesting, a dual-band Coplanar Waveguide(CPW) antenna based on Polyvinylidene Fluoride(PVDF) piezoelectric material is proposed. The main radiation element of the antenna is a rectangular copper sticker. The symmetric L-shaped copper stickers on both sides form a coplanar waveguide feeding structure, which works as a perturbation unit to change the surface current distribution of the antenna, and achieve the dual-band design requirement. The antenna is designed and fabricated on the PVDF piezoelectric film. Due to the piezoelectric properties and high dielectric constant of the piezoelectric material itself, the antenna can simultaneously harvest both Radio Frequency(RF) and vibration energies and the size of the antenna can be effectively reduced. The experimental results show that the antenna can work in the common Industrial Scientific Medical(ISM) band of 2.4 GHz and 5.8 GHz at the same time, and the peak gains are 0.77 dB and 2.47 dB, respectively.

Early blockchain systems mainly run on general-purpose processors with von Neumann architecture such as Central Processing Units(CPUs) and Graphics Processing Units(GPUs). With the development of blockchain technology, it has been widely used and deployed on a large scale in various industries. In computing-intensive and communication-intensive scenarios, both computing energy efficiency and high computing flexibility need to be considered. On this basis, this article uses general-purpose processors such as local computers and cloud servers to form a heterogeneous computing blockchain network, thereby cost-effectively obtaining high-energy-efficiency computing capabilities, excellent compatibility and scalability, etc. The high utilization rate of computing resources provides an underlying technical solution for blockchain computing from homogeneous to heterogeneous, which brings more possibilities for blockchain applications and has broad application prospects.

Criticality analysis method based on Risk Priority Number(RPN) in Failure Mode Effects and Criticality Analysis(FMECA), is usually utilized to identify risk factors, determine the critical failure mode and weak links to provide decision-making basis. In traditional RPN, the evaluation information is deterministic and the correlation of failure modes is not considered. In the actual work, the evaluators can only give uncertain evaluation information due to their own attributes and the level of understanding the evaluation objects. Usually there is correlation between failure modes, which will affect the final hazard analysis results. Aiming at the existing problems, the fuzzy RPN evaluation method based on Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) for the triangular fuzzy evaluation information is studied by using the House of Reliability(HoR) to consider the correlation of failure modes. Finally, the RPN values obtained in different cases are compared with the actual case. The results show that the fuzzy RPN evaluation method considering failure mode correlation can improve the reliability of RPN value and provide more reliable basis for risk decision.

To solve the difficulty of identifying the mixed binary protocol data frames without any prior knowledge, a clustering method based on joint Gaussian Mixture Model(GMM) and auto-encoder is proposed. For the captured unknown binary data frames, firstly its features are extracted via dimension reducing by stacked auto-encoder, and then the optimal number of clusters is obtained according to the corresponding criteria, finally the auto-encoder with modified cost function is utilized to train the binary data frames to improve clustering accuracy. The experimental results show that the accuracy of this method for recognizing the network binary protocol data frames is over 94%.

To cope with the multiple t-test issue in the leakage detection of side-channel power information, a leakage detection scheme is proposed to control the false discovery rate of the multiple t-test process and improve the test effectiveness. Based on the analysis of multiple-hypothesis testing issue, the false discovery rate and test effectiveness are introduced as control parameters in the multiple t-test process. Existing schemes for controlling multiple-hypothesis testing issue are introduced. Combined with the leakage detection process, the method of raising threshold and adjusting the test level is proposed to improve the existing control scheme, which is verified by experiments. The verification results show that this scheme can improve the detection capability of power information leakage and control the test errors within a certain range.

An image conversion method based on generative adversarial networks is proposed in order to solve the problem of different image acquisition costs in different spectral segments. In the conversion process, the image outline does not change into the starting point in the range that can be distinguished by the naked eye. Firstly, the generator and discriminator are trained alternately through pairs of training data, and the loss function is optimized until the Nash equilibrium of the model is reached. Then the test data are utilized to detect the trained model, to check the conversion effect, and to evaluate the conversion effect from the subjective observation and objective calculation of the average absolute error and mean square error. Through the above process, the conversion between different spectral images is finally realized. Among them, the generator learns from U-Net architecture; the traditional convolution neural network architecture is used by the discriminator; and L1 loss function is increased to ensure the integrity of high and low frequency features before and after image conversion. In this paper, the conversion between infrared image and visible image is taken as an example to carry out the experiment. The results show that the conversion between infrared image and visible image can be well realized through the generative adversarial networks designed in this paper.

The radiation effect of several typical power electronic components is analyzed, including single total ionizing dose effect, single neutron radiation effect, the sequence radiation effect of neutron-total dose and the synergic radiation effect of neutron-total dose, to get the failure threshold. Experiments show that electronic components irradiated by both neutron and γ-ray have a lower failure threshold than components radiated by single neutron or γ-ray. The mechanism of synergic enhancement damage in bipolar process devices is analyzed. The main reason is that the ionization damage produces positive oxide charge in the oxide layer of the transistor, which increases the surface potential of the base region, increases the number of interface states, reduces the difference of the carrier concentration on the inner sub-surface of the Si body, and intensifies the degradation of current gain and enhances the transistor neutron displacement damage. It is more practical to evaluate the comprehensive radiation resistance of the device according to the synergic radiation test. The research results are of great significance for the evaluation of the radiation resistance of the device.

In order to realize the synchronous collection of loose particle signals generated inside sealed electronic equipment, a synchronous acquisition system for loose particle signal based on Pulse Per Second(PPS) is designed. Aiming at the problem of poor consistency in the synchronous acquisition of multi-channel loose particle signals, a dual Microcontroller Unit(MCU) control and memory sharing structure is adopted to realize the separation of data reading and data writing during the signal collection process. The external interrupt is triggered by Beidou PPS, and the synchronous collection of the signals in each channel is realized. By setting the Universal Time Coordinated(UCT) as the label of the multi-channel signal collected in a single pulse, the resynchronization of the transmission signal is realized. The test shows that the synchronization error of signal collection in each channel is at the magnitude of microsecond, and the time label of the signal is correct, which provides an important reference for the detection of loose particle signals.

The equivalent circuit model of Dual Polarization Thevenin(DP-Thevenin) is established to describe the dynamic and static characteristics of type 18650 lithium battery. The open circuit voltage and model parameters are identified by constant current pulse discharge experiment and Recursive Least Squares method with Forgetting Factor(FFRLS). Then an equivalent circuit model is built in Simulink, and the impulse current is used as the excitation to verify the model. It is concluded that the response voltage of the model is in good agreement with the actual terminal voltage, with an average error of 1.836%. Next, the hardware circuit of battery experiment is constructed, and the algorithm program is compiled to complete the construction of lithium battery test system. Finally, the performance of State Of Charge(SOC) and State Of Health(SOH) of lithium batteries based on joint algorithm in predicting accuracy and convergence of the algorithm at wrong initial values is analyzed by means of Matlab under random test conditions. The experimental results show that the algorithm can accurately estimate the SOC and internal resistance of batteries, the maximum error is not more than 3.5%. When the initial value differs by 15%, the algorithm can converge to the true value within 319 s with good robustness.

2021 IEEE International conference on Nano/micro Engineered and Molecular Systems (IEEE-NEMS 2021) was held jointly by Xiamen University, Peking University and University of Electronic Science and Technology of China(UESTC) in Xiamen, China during April 25-29, 2021. More than 500 scientists world-wide participated in the conference to share the latest research results. In this review, the current research in nanobiology and nanomedicine, nano/micro sensors/drivers/systems, nanomaterials, nano/micro/molecular manufacturing were introduced. And finally, we summarized and forecasted the development trends of nano/micro technology in the future.