Abstract:With the continuous development of Radio Frequency(RF) devices, terahertz communication is evolving from point-to-point fixed communication experiments to communication applications in the moving state, and terahertz beam reconfigurable technology empowers multi-beam communication systems to provide support for terahertz dynamic communication. This paper introduces the research progress of phased array, meta-surface and other multibeam antennas in terahertz bands. The beam reconfiguration schemes and technical routes of terahertz dynamic communication are sorted out. The basic composition and key technologies of each scheme are analyzed. By discussing the advantages and disadvantages of each technical route, the development blueprint of terahertz dynamic communication system covering low frequency to high frequency is described. Multibeam technology will further accelerate the evolution of terahertz communication to flexible and changeable networking communication forms, and provide a technical foundation for the commercial application of 6G terahertz communication.

Abstract:Terahertz Radio Frequency(RF) devices and communication systems typically exhibit operating bandwidths ranging from several GHz to tens of GHz, with substantial variations in operating characteristics across the band, presenting numerous challenges for performance testing. To address this, this paper proposes an automated measurement method for the terahertz band and designs a versatile automated test system capable of meeting diverse testing requirements, including terahertz RF device and link testing, signal processing algorithm testing, long-distance terahertz channel measurement, and terahertz antenna far-field testing. Measurement results demonstrate that the system can efficiently and accurately complete these various terahertz band testing tasks, not only improving testing efficiency but also providing reliable data support for related research and applications.

Abstract:In response to the issues of functional coupling and low performance in traditional radar digital simulation platforms, an innovative approach has been proposed by integrating the concepts of object-oriented programming and component reuse. This approach introduces a component-based radar simulation architecture. By abstracting the various functions of the radar digital simulation system into independent components and implementing loose coupling communication through a signal-slot mechanism, the components are made mutually independent, thereby enhancing the reusability among them. Simulation verification has demonstrated that this component-based radar digital simulation platform operates accurately, displays normally, and possesses excellent adaptability and scalability. It is capable of meeting diverse simulation requirements and offers a new solution for radar system development.

Abstract:Global Navigation Satellite Systems(GNSS) have been widely applied in mass consumer applications, mechanical control, maritime and aviation navigation, and disaster relief. However, due to the inherent weakness of navigation signals and the open user interface of GNSS, GNSS is highly vulnerable to spoofing attacks. Signal Quality Monitoring(SQM) technology offers a simple yet proven approach for detecting GNSS spoofing attacks. Nevertheless, traditional SQM algorithms are vulnerable to environmental factors, making it difficult to simultaneously satisfy the basic requirements of detection accuracy and timeliness. This paper proposes a spoofing detection algorithm based on the sum of absolute values from the in-phase(I) and quadrature(Q) branches. The implementation logic is simple and requires only minor software modifications to GNSS receivers. Experimental analysis using the spoofing signal dataset from the University of Texas at Austin(TEXBAT) demonstrates that, compared with conventional SQM algorithms, the proposed algorithm achieves faster detection speed and superior performance in detecting spoofing attacks.

Abstract:In an electromagnetic countermeasure environment, communication behavior recognition is a crucial component of signal mining and utilization in electromagnetic space. Under non-cooperative conditions where prior information is difficult to obtain, acquiring electronic intelligence requires performing signal feature analysis on reconnaissance-acquired communication data and conducting communication behavior recognition. Based on extensive analysis of domestic and foreign literatures, this paper summarizes the definitions and classifications of communication behavior oriented toward non-cooperative wireless networks, introduces the concept and primary approaches of communication behavior recognition, and categorizes various existing implementation methods along with their characteristics. Finally, it concludes the main problems and challenges in current communication behavior recognition research and prospects potential future development directions for this technology.

Abstract:To realize low-profile dual-polarized broadside radiation, a 1×8 dual-polarized low-profile slot antenna array fed by Substrate-Integrated Coaxial Line(SICL) is proposed. Unlike conventional dual-polarized arrays in which two independent feeding networks are vertically stacked, the radiating elements and the feeding network of the proposed design are arranged in the same layer, drastically reducing the overall profile. Measured results show that the array achieves a -10 dB impedance bandwidth in 25.8~27.4 GHz and an isolation higher than 20 dB. Benefiting from its simple feeding topology, low profile, and low fabrication cost, the proposed approach is highly attractive for future large-scale, high-gain, dual-polarized millimeter-wave applications.

Abstract:To address the issue of narrow bandwidth in existing Multiple-Input Multiple-Output(MIMO) microstrip antennas, a compact broadband high-isolation MIMO microstrip antenna and array design is proposed. By introducing cross-type capacitive fences, open slots, and shorting vias, the isolation between ports is gradually enhanced. To validate this concept, a 1×2 microstrip antenna and a 2×2 array were fabricated and measured. The measured impedance bandwidth of the 2×2 array is 44.9%, with an isolation better than 21.8 dB, showing good consistency between the measured and simulated results. Based on the 2×2 array, the applicability of the proposed decoupling structure in large-scale arrays was further verified. Simulation results indicate that the impedance bandwidth of a 2×6 array is 43.5%, with an isolation of at least 20 dB within the operating bandwidth. Therefore, the proposed decoupling solution has broad application prospects in broadband high-isolation MIMO applications and antenna arrays.

Abstract:To address the difficulty in predicting the Passive Intermodulation(PIM) of waveguide flange connections caused by micro-contact randomness, the fluctuations in both PIM and DC contact resistance at these joints are investigated. Experimental results demonstrate that, upon repeated connections, both PIM and DC contact resistance exhibit stochastic variations; moreover, the PIM level generally decreases as the DC contact resistance diminishes. To uncover the origins of these fluctuations, a Monte Carlo model for PIM at waveguide flanges is developed based on existing theories of PIM in electrical contacts. The numerical results agree well with experimental data, theoretically confirming that the random distribution of contact pressure during successive connections is a primary source of PIM variability in waveguide flange joints.

Abstract:Transmission lines are often employed in complex metal shielded cavities, where numerical algorithm-based simulations are memory-intensive and computationally inefficient. This paper proposes a novel method for simulating the electromagnetic transient response of transmission line load coupling problems, which combines an improved Locally One-Dimensional Finite-Difference Time-Domain(LOD-FDTD) method with Modified Nodal Analysis(MNA) to more efficiently and accurately solve field-line-circuit electromagnetic problems at fine structural gaps. Gaps and cables are treated as electromagnetic structures, and the improved LOD-FDTD method is employed to solve the field distribution; based on the circuit substitution principle, MNA is employed to analyze the port voltages and currents of circuit modules, thereby completing the hybrid calculation of transient electromagnetic response for the entire field-line-circuit coupling problem. Simulation results demonstrate that, compared to other analysis methods, the proposed approach substantially enhances computational efficiency while maintaining reasonable numerical accuracy.

Abstract:To address the challenges of privacy protection in recognizing unsafe behaviors such as falls and low cross-environment recognition rates, this paper proposes a behavior recognition framework, Single-antenna Cross-environment Stable Human Activity Feature Extraction and Recognition Framework (SSRF), based on Channel State Information(CSI), optimized from the existing ReWiS model. By collecting data on five types of elderly behaviors(such as falls, no action, etc.) from different environments, the CSI signals are normalized, followed by Singular Value Decomposition(SVD) and Pearson correlation coefficient calculation to generate labeled CSI data samples, which are then fed into the ProtoNet model for classification. Compared to ReWiS, SSRF significantly reduces the number of parameters(from 111 936 to 37 392) and accelerates both training and testing speed, with total training time reduced from 33.12 s to 26.8 s, and per-sample testing time reduced from 0.000 149 s to 0.000 104 s. In the four-category task of a public dataset and the five-category task of a custom dataset, SSRF achieves average cross-environment recognition accuracies of 89% and 85%, respectively, with 95% accuracy for fall detection. Experimental results show that SSRF maintains high generalization performance while significantly improving the efficiency.

Abstract:A reduced-dimension Propagation Method(PM) based algorithm is proposed for central Direction-Of-Arrival(DOA) estimation of coherent distributed sources. Based on the Generalized Array Manifold(GAM) model, the central DOA can be decoupled from the original array manifold, achieving separation from the angular spread. To avoid eigenvalue decomposition of the sample covariance matrix, a propagation operator matrix is computed in the orthogonal space of the generalized array steering vectors to construct the objective function. With the introduced dimensionality reduction technique, the proposed algorithm only requires a one-dimensional spectral peak search to determine the central DOA of the sources, which significantly reduces the computational complexity. Additionally, the Cramér-Rao lower Bound(CRB) for this scenario is derived in detail to provide a benchmark for the estimation performance of the algorithm. Performance analysis and numerical simulation results demonstrate that the proposed algorithm maintains excellent angle estimation performance while reducing complexity.

Abstract:For individual radiation source identification technology using deep neural networks, network depth is continuously increased to achieve good recognition performance, resulting in an explosion of model parameters and computational complexity, which makes deployment difficult on resource-constrained edge devices. To address this, this paper proposes a network architecture called ODCNet(One-Dimensional Depthwise Separable Convolution Network) based on one-dimensional depthwise separable convolution and one-dimensional convolutional block attention modules. By combining depthwise and pointwise convolutions, one-dimensional depthwise separable convolution effectively reduces model parameters and computational complexity. The lightweight one-dimensional convolutional block attention module can effectively enhance model performance and ensure recognition capability. Experimental results show that ODCNet's recognition performance is comparable to MobileNet V3, while its parameters are only 11.27% of MobileNet V3's, its computational complexity is 17.49% of MobileNet V3's, and its inference time is reduced to 50% of MobileNet V3's.

Abstract:The rapid development of mobile communication technology has generated abundant unlabeled radio source signals. To fully utilize unlabeled data, this paper proposes an Independence Criterion-based Unsupervised Domain Adaptation(ICUDA) method for specific emitter identification. The independence criterion is employed to measure the similarity between the source domain and the target domain, and combined with an improved convolutional neural network to transfer knowledge from the source domain to the target domain, thereby helping improve the classification performance of the target domain that contains only unlabeled data. Under seven transfer scenarios constructed based on Software-Defined Radio(SDR) dataset collected in a laboratory environment, compared with baseline methods and three unsupervised domain adaptation methods, the proposed method achieves the best classification performance in the target domain across all scenarios, with an average recognition accuracy of 84.2%, demonstrating that the proposed method can extract features with good inter-class separability and intra-class compactness on the target domain, effectively reducing the target domain's dependence on high-quality labeled data.