Abstract:Existing bistatic Synthetic Aperture Radar(BiSAR) imaging methods simplify all targets in the scene as the superposition of point targets, ignoring the complex scattering processes of the targets. This leads to model mismatch in the imaging echoes and the loss of structural information of the targets in the imaging results. To address this issue, a BiSAR target parametric imaging method based on target scattering feature parameter estimation is proposed. Firstly, a BiSAR parametric echo model is established based on the ideal point scattering model and three typical target scattering models. Secondly, the position parameters are estimated using the coarse imaging results, and a local dictionary matrix is constructed based on the parametric echo model to achieve dictionary dimensionality reduction. Then, the Alternating Direction Method of Multipliers(ADMM) is utilized to estimate the target scattering feature parameters. Finally, the image is parametrically reconstructed based on the estimated target parameters. Experiments with simulated data validate the accuracy and effectiveness of the proposed method, which can enhance the structural information of the targets in the imaging results.

Abstract:Aiming at the problem of weak target detection in traditional single-site radar, an integrated target detection and parameter estimation algorithm based on distributed waveform diversity array radar is proposed. The distributed waveform diversity array radar system consists of Frequency Diverse Array(FDA)-Multiple Input Multiple Output(MIMO) radar and Element Pulse Coding(EPC)-MIMO radar. Under the background of white noise, a binary hypothesis problem is constructed for each local radar station, where the target angle and distance information are unknown. Based on the above model, an adaptive detector is designed under the Generalized Likelihood Ratio Test(GLRT) criterion. The Coordinate Descent(CD) method and Gradient Projection Method(GPM) are employed to solve the estimates of target angle and distance information and obtain local detection statistics. Subsequently, at the fusion center, the global detection statistic for the final detection decision is constructed by calculating the logarithmic sum of multiple sets of local detection statistics. Simulation experiments verify that compared with single-site waveform diversity array radar, the distributed waveform diversity array radar improves the target detection probability. Compared with distributed phased array radar, the distributed waveform diversity array radar significantly enhances the target detection probability and parameter estimation performance.

Abstract:To meet the demanding requirements of land and ocean observation under harsh and hazardous conditions, a compact, cost-effective, high-precision interferometric miniature Synthetic Aperture Radar(MiniSAR) system tailored for low, slow and small(LSS) platforms is proposed. Leveraging the unique characteristics and advantages of LSS platforms, the imaging modes, parameter design, and system architecture of the MiniSAR are discussed. In addition, the key technical challenges inherent to the MiniSAR system are analyzed, classified, and thoroughly evaluated. The findings provide both a theoretical foundation and technical support for system development, while offering valuable guidance for the design of lightweight, low-power MiniSAR solutions.

Abstract:With the global deployment of the BeiDou navigation system, Passive Radar(PR) has gained access to all-weather, continuous, and wide-coverage opportunistic signals, demonstrating significant potential for maritime surveillance. However, the extremely low signal-to-noise ratio and nonlinear spatio-temporal variations challenge the effectiveness of conventional radar localization and tracking methods. A Joint Localization and Tracking(JLT) method is proposed for maritime targets by fusing multi-satellite illuminators. The approach involves long-time coherent integration of echoes into the Difference Bistatic Range(DBR),Doppler Centroid(DC),and Doppler Frequency Rate(DFR) domain. Leveraging the consistency of DFR, the multi-static results are projected and non-coherently fused in the X- Y- V _r domain to achieve initial target localization and velocity estimation. Subsequently, a Particle Filter(PF) is employed to address the nonlinear measurement model of PR, enabling precise multi-satellite tracking of moving targets. Simulation results confirm that the proposed method enhances the performance of state estimation and significantly reduces trajectory estimation errors.

Abstract:Gesture recognition using millimeter-wave(mmWave) radar offers advantages such as contact-free operation, high detection accuracy, privacy preservation, and robust environmental adaptability, making it promising for industrial human-machine interaction and smart-home applications. However, existing mmWave-based dynamic-gesture recognition approaches suffer from high model complexity, large computational cost, low accuracy, and slow inference speed. To address these challenges, a lightweight gesture-recognition method is proposed based on an improved MobileViT network that maintains high accuracy while significantly reducing computational complexity for deployment on embedded devices. Firstly, dynamic-gesture echoes are captured with an mmWave radar. After suppressing device noise and background clutter, the data are reorganized into a 3-D matrix(sample points×chirps×frames). Range-time and Doppler-time maps are then generated via Fourier transform and fed into the enhanced MobileViT model for feature extraction and fusion, yielding the final gesture classification. Experimental results show that the proposed MobileViT model has only 0.167 M of parameter space complexity and 0.253 GFLOPs of computational complexity. Evaluated on a 12-class dynamic-gesture dataset, the method achieves 99.31% of recognition accuracy, demonstrating its effectiveness.

Abstract:To further exploit the dual advantages of spatial diversity and waveform diversity in distributed frequency diverse array radar, a joint optimization method for transmit and receive parameters of distributed frequency diverse array radar based on the Maximum Block Improvement-Minorize Maximization(MBI-MM) is proposed. This method enhances the radar system's detection performance by jointly designing the transmit frequency offsets and receive filters of each radar station. Firstly, an optimization function is constructed to maximize the Signal-to-Interference-plus-Noise Ratio(SINR) of the target output in the clutter background, with constraints imposed on the frequency offsets and receive filters. Then, the original problem is decomposed into two independent subproblems, which are solved alternately. Finally, the MBI method is employed to iteratively update the optimization solutions of the two subproblems until the objective function converges. Simulations demonstrate that the joint optimization of frequency offsets and receive filters can significantly improve the SINR of the target output in the clutter background. Specifically, the target output SINR is improved by 5.7 dB when only the frequency offsets are optimized compared to when only the receive filters are optimized. After the joint optimization of transmit and receive parameters, the target output SINR is increased by 7.2 dB compared to when only the receive filters are optimized, and by an additional 1.5 dB compared to when only the frequency offsets are optimized. The proposed method enhances the radar's target detection performance in complex backgrounds for specific areas.

Abstract:Terahertz(THz) Quantum Cascade Lasers(QCLs) are semiconductor devices based on intersubband electron leaps, which are characterized by small size, lightweight, easy integration, tunable frequency and high energy conversion efficiency, and have become one of the hotspots of current research. At present, THz QCLs have not appeared high-performance devices, and electron transport, as the most basic and fundamental starting point of THz QCLs, is an important way to break through this bottleneck. In this paper, the research progress in electron transport for THz QCLs is sorted out. Firstly, the influence of various scattering on the performance of THz QCLs is discussed. Secondly, the method to improve the operating temperature of QCLs through the optimized design of active region is also introduced. Finally, the research on electron transport of THz QCLs is outlooked. This work shows certain reference significance for improving the performance and application of THz QCLs.

Abstract:Dielectric characterization of materials is crucial for accurately assessing their terahertz electromagnetic properties and ensuring their effectiveness in relevant applications. A method is proposed for dielectric property testing of Materials Under Test(MUT) based on their self-resonant characteristics, combining electronic terahertz technology with waveguide aperture reflection. The electromagnetic process and influencing factors of the self-resonance of the MUT at the rectangular waveguide aperture are clarified using the Finite-Difference Time-Domain(FDTD) method for electromagnetic field numerical analysis. Subsequently, the relationship between the waveguide reflection coefficient and the dielectric properties of the MUT is obtained. Based on the frequency shift of the material's self-resonance, grooves and etched channels are created on a single-crystal silicon wafer which is then bonded with polydimethylsiloxane(PDMS) to measure the dielectric properties of the liquid within the channels. Finally, a testing system with a single-crystal silicon wafer as the MUT is constructed in the laboratory according to the simulation rules, verifying the reliability of the simulation results and the feasibility of the proposed dielectric testing method.

Abstract:In order to obtain the microscopic characteristics inside the lightning channel, a lightning dual band optical observation system is designed. The system mainly consists of a signal capture module, a signal reception and conditioning module, a signal acquisition module, and a power supply module. The transmission coefficient of the system can be calibrated by using a standard light source. The system can directly observe the time-domain full wave waveforms of different wavelength optical signals, and can analyze the temperature and conductivity information of channels through relative spectral line intensity. Based on the similarity between gap discharge and lightning, the designed system is employed to observe the gap discharge channel, and the temperature and conductivity of the gap discharge channel are calculated. The results indicate that the temperature variation range of the gap discharge channel in this experiment is 5 400~5 980 K, and the conductivity variation range is 10.93~28.53 S/m.

Abstract:In the process of detecting the phase performance of the quiet zone in a compact range, it is necessary to eliminate the influence of the detection system on the measurement results in order to obtain the true performance of the site. Studies have shown that the detection phase error is caused by the installation offset of the scanning frame and the offset of the detection plane, and it exhibits certain regularities. In this paper, the installation offset of the scanning frame and the offset of the detection plane are calculated based on the phase measurement results of different sections of the quiet zone, and then compensation and correction are performed to eliminate the influence brought by the deviation of the detection system's measurement trajectory from the ideal path. This method is simple, reliable, and capable of obtaining real and effective detection results, which has been applied to the performance detection of the quiet zone in a compact range.

Abstract:The electromagnetic environment of the working frequency band of High-Frequency Surface Wave Radar(HFSWR) is extremely complex. Noise, including radio frequency interference, sea clutter, and ionospheric clutter, can severely affect the accuracy of ship target identification. To address this issue, an improved feedforward Denoising Convolutional Neural Network(DnCNN) is proposed to suppress the noise in HFSWR marine echo signals. Based on the characteristics of the noise in HFSWR marine echo signals, the original DnCNN is modified in terms of patch size, convolutional kernel size, and network depth to make it suitable for the HFSWR denoising task. A dataset containing 10 000 pairs of Range-Doppler(RD) spectra is generated based on HFSWR sea trial data and is evenly divided into training and testing sets. Analysis of the denoising results of three groups of RD spectra in the testing set(with sea clutter, radio frequency interference, and ionospheric clutter as the main noise sources, respectively) shows that the improved DnCNN model significantly outperforms the original DnCNN in both noise suppression and maintaining the amplitude of ship target signals. Moreover,statistical results of the entire testing set indicate that the Peak Signal-to-Noise Ratio(PSNR) of the improved DnCNN denoising metric is 44.13 dB on average, which is significantly higher than the 35.58 dB of the original DnCNN. In summary, the improved DnCNN effectively suppresses the noise in HFSWR marine echoes while well preserving the amplitude of ship target signals.

Abstract:To address the high computational complexity and low combining gain of conventional multi-antenna joint reception algorithms under low-SNR(Signal-to-Noise Ratio) conditions, a time-frequency phase-difference compensation-based multi-antenna joint signal-reception and combining algorithm is proposed. Building on the Semi-Implicit Method for Pressure Linked Equations(SIMPLE) framework applied to the Cross-Ambiguity Function(CAF) and phase-difference estimation, the algorithm first performs phase-error correction and then applies weighted combining, effectively lowering the computational burden on the signal-processing hardware. Simulation and experimental results demonstrate that, for two-channel signals exhibiting time-frequency phase differences, the proposed algorithm achieves perfect post-compensation alignment, confirming its feasibility. Under practical SNRs of 7 dB and 9 dB, the measured receive gain improves by approximately 2.76 dB, and the joint reception gain reaches 92% of the theoretical maximum, delivering the high post-combining gain required and significantly enhancing the reception of extremely weak signals. The approach offers a novel solution for acquiring ultra-faint deep-space signals.

Abstract:To achieve accurate monitoring of electrical faults in propane dehydrogenation units, an automated noise-exceedance detection technique based on multi-channel acoustic-signal denoising is proposed. Using an Advanced Reduced Instruction Set Computer(ARM) platform, the system assesses noise levels within the units in real-time while capturing sound signals via a multi-channel acquisition scheme. An improved Ensemble Empirical Mode Decomposition(EEMD) method, aided by information fusion, is employed for multi-channel denoising.Wavelet-packet analysis is then applied to extract acoustic features, which are subsequently normalized. A multi channel Convolutional Neural Network(CNN) is constructed to model electrical-fault detection for the propane dehydrogenation units. Experimental results demonstrate that the proposed technique effectively monitors and denoises electrical noise, raising automatic monitoring accuracy to above 0.8.

Abstract:Aiming at the problem that the failures of overvoltage protection device are difficult to identify under different levels of noise, and that failure details and device color information are hard to distinguish, a method for automated recognition of the failures of overvoltage protection device based on infrared imaging and Support Vector Machine(SVM) has been studied. Infrared imaging technology is employed to generate infrared thermal images of the overvoltage protection devices. An infrared thermal image enhancement method based on multi-scale Retinex is employed to improve the images. A fault feature extraction method based on wavelet transform is adopted to obtain the fault features of the overvoltage protection devices. A binary tree algorithm is applied to optimize the SVM, resulting in an SVM binary tree classification model. Automated recognition of the failures of overvoltage protection device is achieved through model training and solving. Experimental results show that the enhanced infrared thermal images of the overvoltage protection devices retain good detail and color information, with a Peak Signal-to-Noise Ratio(PSNR) increased by more than 50 dB. The extracted fault features of the overvoltage protection devices have good separability. The automated recognition match rate of the failures of overvoltage protection device under different noise levels remains above 96%, allowing for accurate identification of the failures.