Dong Wang

In Global Navigation Satellite System (GNSS)-denied environments, positioning accuracy is critical for unmanned aerial vehicle (UAV) swarms. A particle filter-based cooperative localization algorithm is proposed to improve UAV positioning accuracy. To address the issues of particle impoverishment and loss of diversity inherent in traditional particle filters, a Selective Correction Adaptive Particle Filter (SCA-PF) with an enhanced resampling strategy is introduced. First, the algorithm incorporates a threshold-based resampling improvement to mitigate particle impoverishment common in conventional particle filters. Second, an adaptive threshold feedback strategy, based on the residual variation rate, dynamically adjusts the weight threshold to more effectively select high-weight particles. Additionally, a firefly-inspired attraction mechanism is applied to fine-tune neighboring particles, thereby improving convergence and robustness under complex noise conditions. Simulation results demonstrate that the adaptive threshold strategy increases localization accuracy by an additional 10% over fixed-threshold methods. The proposed SCA-PF enhances cooperative navigation accuracy by approximately 15% compared to other resampling-based algorithms.

In recent years, the Cyclone Global Navigation Satellite System (CYGNSS) of NASA has attracted widespread attention for the retrieval of freeze/thaw (F/T) states through the analysis of reflected signals. F/T variations in high-altitude regions have long been a focal point in this field. However, these areas lack benchmark observational data with high temporal and spatial resolution. A model named Partial Convolution–Time Convolutional Network (PTCN) is proposed in this paper to reconstruct CYGNSS data at a 3 km resolution. This model integrates partial convolution with a time convolutional network (TCN) and does not rely on any auxiliary data. Partial convolution is employed to distinguish valid pixels, with the interference of missing values being removed. TCN is employed to capture temporal features, which results in the reconstruction of observational data. Compared with the original observational data (at a 3 km resolution), the coverage of the reconstructed data is six times that of the original. A simulation of missing data is applied for the first time in the quantitative evaluation of observational data reconstruction. The results show that the value of R for the reconstructed data reaches 0.92, and the value of the root mean square error (RMSE) reaches 2.7. The reconstructed data is used for daily F/T retrieval. At both 36 km and 9 km resolutions, the F/T retrieval accuracy after reconstruction is comparable to that before reconstruction. The temporal resolution is improved by 256%, which successfully fills 92% of the observational gaps in soil moisture passive–active (SMAP) data. Compared with ground-based F/T retrievals, the reconstructed F/T accuracies are 87.71% at 36 km and 82.3% at 9 km.The model successfully reconstructs high-temporal and spatial resolution CYGNSS data while maintaining accuracy. In the future, this method holds significant potential for the application of global GNSS-R high-temporal and spatial resolution remote sensing observations.

In this paper, a terahertz temperature-controlled switch (TCS) composed of VO ₂ and Cu is proposed. The dual-state bandwidth and amplitude perform excellently, an overlapping bandwidth of 79%, and a modulation depth of over 90%.

A broadband metamaterial absorber (MA) is proposed in this paper, whose parameters and absorption spectra are optimized and predicted by the back propagation (BP) neural network (NN). This MA realizes the independent modulation of absorbance and bandwidth using vanadium dioxide (VO₂) and graphene. When the conductivity of VO₂ is 2 × 10⁵ S m⁻¹ with the E_f of graphene at 1 eV, the MA can achieve more than 95% absorbance within 2.16–6.23 THz. Moreover, the absorbance modulation can be realized by temperature control with a modulation depth of 61.48%, and bandwidth modulation can be realized by voltage control with a modulation depth of 37.35%. The proposed MA allows for the modulation of both absorbance and bandwidth, addressing the limitations of modulation dimensions and presenting a new design approach for flexibly tunable MAs. Furthermore, by the use of the BP NN, the optimization of the structure can be achieved more efficiently.

A THz metamaterial absorber can realize broadband perfect absorption ( A > 99%, B = 3.14 THz) and RI sensing.

Compressed Sensing SAR Imaging is based on an accurate observation matrix. As the observed scene enlarges, the resource consumption of the method increases exponentially. In this paper, we propose a weighted ℓ_2/3-norm regularization SAR imaging method based on approximate observation. Initially, to address the issues brought by the precise observation model, we employ an approximate observation operator based on the Chirp Scaling Algorithm as a substitute. Existing approximate observation models typically utilize ℓ_q(q = 1, 1/2)-norm regularization for sparse constraints in imaging. However, these models are not sufficiently effective in terms of sparsity and imaging detail. Finally, to overcome the aforementioned issues, we apply ℓ_2/3 regularization, which aligns with the natural image gradient distribution, and further constrain it using a weighted matrix. This method enhances the sparsity of the algorithm and balances the detail insufficiency caused by the penalty term. Experimental results demonstrate the excellent performance of the proposed method.

An ultra-broadband and double-broadband terahertz metamaterial absorber is realized using photoconductive silicon and an innovative planar resonant structure design, greatly broadening the absorption bandwidth of silicon-based absorbers.

The number of Printed Circuit Board (PCB) layers is continually increasing with the increase in data transmission rates, and the Signal Integrity (SI) of high-speed digital systems cannot be ignored. Introducing Vertical Interconnect Accesses (VIAs) in PCBs can realize the electrical connection between the top layer and the inner layers, however, VIAs represent one of the most important reasons for discontinuity between the PCBs and package. In this paper, a new optimization scheme for a differential VIA stub is proposed, with 3D full-wave numerical simulation used for modeling and simulation. Results show that this scheme optimizes the return loss and insertion loss while making the signal eye diagram more ideal, which can improve the transmission effect of high-speed signals.

The device offers robustness and polarization insensitivity, providing versatility and adaptability in various applications and environmental conditions with its two absorption modes and total reflector, which can be flexibly switched.