State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System

Orientation angle compensation was incorporated into model-based decomposition to cure overestimation of the volume scattering contribution for interpretation of polarimetric synthetic aperture radar (PolSAR) data. The compensation is based on rotating the coherency matrix to minimize the cross-polarization term. However, this processing cannot always guarantee that the double- and odd-bounce scattering components will be rotated back to zero orientation angle and left with zero cross-polarization power. As a result, built-up patches with large orientation angles may still suffer from the scattering mechanism ambiguity. In this paper, double- and odd-bounce scattering models were generalized to fit the cross-polarization and off-diagonal terms, by separating their independent orientation angles. A general decomposition framework is proposed that utilizes all elements of a coherency matrix. The residual minimization criterion is used for model inversion. All the model parameters are simultaneously obtained using a nonlinear least squares optimization technique. The manual intervention, branch conditions, and negative power issues are avoided. The performance and advantages of this approach are demonstrated and evaluated with spaceborne L-band ALOS/PALSAR and airborne X-band Pi-SAR PolSAR data sets. Comparison studies are also carried out and demonstrate that further improved decomposition performance is achieved by the proposed method, especially in oriented built-up areas.

Recent advances in scattering modeling and model-based decomposition theorem were reviewed. The notable achievements include orientation compensation processing, nonnegative eigenvalue constraint, generalized scattering models, complete information utilization, full-parameter inversion strategy, and the polarimetric-interferometric decomposition scheme. These advances contribute to make scattering models more adaptive, better fit observations and guarantee physically meaningful decomposition solutions. The key features of these advances have been summarized. Performance evaluation and further development perspectives were also discussed. One promising way is to fuse multiple data to better model scattering mechanisms, such as the polarimetric-interferometric modeling attempts. Besides, with the progress in PolSAR sensors, imaging modes (e.g., bistatic, hybrid-polarization and multi-incident-angle modes) and application requirements, the development of specific scattering mechanism interpretation techniques, multiangular decomposition, and compact/hybrid decomposition techniques are also highly preferred.

The overestimation of volume scattering power and the scattering mechanism ambiguity are still present in model-based decompositions even with the implementation of the deorientation processing. These effects are demonstrated and investigated. One possible reason is because of the limited dynamic range of the models themselves that are not fully satisfied for the mixed scene cases. An empirical volume scattering model is proposed, using the repeat-pass polarimetric synthetic aperture radar interferometry (PolInSAR) coherence, to extend the model dynamic range to be more adaptive. PolInSAR coherence is sensitive to different types of forests and terrains. The proposed model inherits these characteristics. In addition, it considers the cross-polarization power induced by oriented man-made structures. Thereby, a model-based polarimetric decomposition scheme is developed. The efficiency of the proposed method is demonstrated using E-SAR airborne and ALOS/PALSAR spaceborne repeat-pass PolInSAR datasets. Comparative experiments are carried out and show that the proposed decomposition overcomes the scattering mechanism ambiguity between forests and oriented built-up areas, since it successfully identifies the oriented buildings as double- or odd-bounce man-made structures while keeping the volume scattering dominant for the forests. Besides, the stable decomposition performance over the oriented built-up patches with quite different orientation angles also validates the improvement of the proposed decomposition. In addition, the demonstrations with short and long temporal baselines validate the generality of the proposed method.

Bistatic inverse synthetic aperture radar (ISAR) operates with spatially separated transmitting and receiving antennas. This study presents a method capable of generating deceptive images from a series of intercepted bistatic ISAR chirp pulses. It is demonstrated that deceptive false‐target images will be induced by the under‐sampled pulses which are retransmitted to a moving target and scattered by it under the principles of bistatic ISAR configuration. Additionally, the jamming idea is proved to be applicable based on the characteristics of the false‐target images and the requirement of jamming power. A scattering model of Yak‐42 plane with 330 point scatterers is adopted to verify the effectiveness of the jamming idea.

Automatic modulation classification of radar signals, which plays a significant role in both civilian and military applications, is researched in this study through a deep learning network. In this study, a novel network combined a shallow convolution neural network (CNN), long short‐term memory (LSTM) network and deep neural network (DNN) is proposed to recognise six types of radar signals with different signal‐to‐noise ratio (SNR) levels from −14 to 20 dB. First, raw signal sequences in the time domain, frequency domain and autocorrelation domain are as input for a shallow CNN. Then the features extracted by CNN will be the input of LSTM network. Finally, DNNs will output the signal modulation types directly. The simulation results demonstrate that the accuracies in autocorrelation domain are all more than 90% at −6 dB and close to 100% when SNR > −2 dB. The recognition performances of the three domains are compared. Compared with other recognition methods, the proposed method has higher average accuracy and better performance under low SNR condition. The measured results show that the proposed method has achieved high accuracies of common four kinds of measured radar signals.

Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.

Abstract The relatively large non‐radiative recombination energy loss (Δ E 3 ) is the main source of energy losses in organic solar cells (OSCs). The energetic disorder plays a crucial role in non‐radiative energy losses; however, reducing the energetic disorder by modifying terminal groups has rarely been investigated. Herein, four acceptors, BTP‐ICB1F, BTP‐ICB2F, BTP‐ICB3F, and BTP‐ICBCF3, with fluorinated phenyl terminal groups are reported at identified substitution sites. The theoretical and experimental results show that this system possesses smaller energetic disorder than the generally‐used Y6 acceptor due to the strong electron polarization effect arising from tight, 3D molecular packing. Therefore, the PM6:BTP‐ICBCF3 combination achieves high efficiency of 17.8% with high open circuit voltage ( V OC ) of 0.93 V and ultralow Δ E 3 of 0.18 eV, which is the smallest Δ E 3 for the binary OSCs with power conversion efficiency (PCEs) over 17% reported to date. Lastly, using the ternary strategy by incorporating the BTP‐ICBCF3 acceptor into PM6:BTP‐eC9, a higher PCE of 18.2% is achieved with enhanced V OC . The results imply that introducing new terminal groups in acceptors is promising for reducing energetic disorder and energy losses.

The sine waveguide (SWG) is presented as a potential slow-wave structure (SWS) for the millimeter-wave (mm-wave) and terahertz (THz) traveling-wave tube (TWT) because of the advantages of wide bandwidth, natural electron beam tunnel, and easy fabrication. In particular, this theoretical study indicates that the transmission loss of the SWG is far less than that of the folded waveguide SWS, which is widely employed in the mm-wave and THz TWT. Here, the flat-roofed SWG slow-wave circuit working in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${W}$ </tex-math></inline-formula> -band has been machined using nano-computer numerical control technology, which guarantees micron tolerances and surface roughness of tens of nanometers using carbide tooling with 100- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mu }\text{m}$ </tex-math></inline-formula> diameter. The cold test results show that the transmission coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${S}_{{{21}}}$ </tex-math></inline-formula> of the entire high-frequency slow-wave circuit is more than −4.1 dB with a total length of 123.84 mm in the frequency range from 90 to 100 GHz, and the transmission loss is less than 0.36 dB/cm.

This paper reports on a sensor based on multi-element complementary split-ring resonator for the measurement of liquid materials. The resonator consists of three split rings for improved measurement sensitivity. A hole is fabricated at the centre of the rings to accommodate a hollow glass tube, through which the liquid sample can be injected. Electromagnetic simulations demonstrate that both the resonant frequency and quality factor of the sensor vary considerably with the dielectric constant and loss tangent of the liquid sample. The volume ratio between the liquid sample and glass tube is 0.36, yielding great sensitivity in the measured results for high loss liquids. Compared to the design based on rectangular split rings, the proposed ring structure offers 37% larger frequency shifts and 9.1% greater resonant dips. The relationship between dielectric constant, loss tangent, measured quality factor and resonant frequency is derived. Experimental verification is conducted using ethanol solution with different concentrations. The measurement accuracy is calculated to be within 2.8%, and this validates the proposed approach.

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available at <uri>https://github.com/huizhangyang/BSF</uri>.

The realistic false targets caused by interrupted sampling repeater jamming (ISRJ) can mask the real target, leading to the failure of radar target detection. In this letter, a method based on jointly designing complementary sequences and receiving filters under the signal-to-noise ratio loss constraint is proposed to suppress ISRJ. A gradient-based nonlinear programming solver and the Lagrange multiplier method are used to optimize the sequences and filters alternately. Numerical results show that the proposed method can generate sequences and receiving filters with good correlation properties and anti-ISRJ performance.

A dual-band dual-rotational-direction reflective linear-to-circular polarization converter based on metasurface is designed, fabricated, and measured. The unit cell consists of an open ring and a square patch. The open ring creates two resonances, and the square patch is used to improve the axial ratio (AR). It is shown that this design can be working at two frequency bands, i.e., 29.0–41.5 GHz and 52.5–61.5 GHz. Interestingly, it is found that the linearly polarized wave in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x(y)$ </tex-math></inline-formula> -direction can be converted into right(left)-handed circularly polarized wave at the former band, and into left(right)-handed circularly polarized wave at the later band. Compared to other designs in the literature, this design demonstrates 45° angular stability for 3 dB AR over two operational bands. In addition, this design is realized on a single substrate, making it easier to be fabricated. Furthermore, the insertion loss can be as low as 0.5 dB, showing a very low-loss property. Lastly, the unit cell is less than 0.2 wavelength at the lower frequency band. The measured results show good agreement with simulation. Potential applications can be envisaged in dual-band and dual-polarization communication.

Synthetic aperture radar (SAR) image target detection methods based on semi-supervised learning, such as the mean teacher framework, have shown promise in diminishing the issue of limited labeled data. However, several challenges exist in current methods. Firstly, data augmentation techniques designed for optical images may not be suitable for SAR images due to differences in imaging methods. Additionally, the contribution of pseudo labels remains constant during the initial retraining stage can lead to degradation in prediction results. Moreover, the low quality of predicted bounding boxes poses a challenge for effective retraining. To address these challenges, we propose an end-to-end semi-supervised detection method based on the mean teacher framework. To enhance the robustness of training, we firstly introduce SAR-specific data augmentation techniques, including multiplicative noise, which effectively increase the diversity of training samples. Secondly, we propose a method that weights the losses of pseudo-labeled data using a hard-sigmoid function, gradually improving the importance of pseudo-labeled data during retraining, thereby alleviating their potential negative impact on the training process. Finally, we propose an IoU-aware subnetwork to incorporate high-quality pseudo-labeled bounding boxes into retraining, allowing them to contribute to network adjustments while mitigating the impact of low-quality samples. Experimental evaluations on publicly available SAR image datasets demonstrate the effectiveness of our proposed method in improving the target detection capability of semi-supervised SAR target detection.

We propose a simple and compact design of a patch antenna with the frequency-reconfigurability function. Six p-i-n diodes are positioned symmetrically along the nonradiating edges to allow the selection of one frequency band among 36 different states. One patch edge is shorted to the ground plane to reduce the antenna footprint. The antenna measured operating frequency ranges from 2.35 to 3.43 GHz. The measured peak gain and the measured peak efficiency are 4.3 dBi and 73%, respectively. The antenna has a compact size, preserves stable and unidirectional radiation patterns, uses a simple dc bias circuit, and is printed on a single-layer substrate. Hence, it is suitable for wireless applications requiring miniaturized size and acceptable performances.

Building extraction is a popular topic in remote sensing image processing. Efficient building extraction algorithms can identify and segment building areas to provide informative data for downstream tasks. Currently, building extraction is mainly achieved by deep convolutional neural networks (CNNs) based on the U-shaped encoder–decoder architecture. However, the local perceptive field of the convolutional operation poses a challenge for CNNs to fully capture the semantic information of large buildings, especially in high-resolution remote sensing images. Considering the recent success of the Transformer in computer vision tasks, in this paper, first we propose a shifted-window (swin) Transformer-based encoding booster. The proposed encoding booster includes a swin Transformer pyramid containing patch merging layers for down-sampling, which enables our encoding booster to extract semantics from multi-level features at different scales. Most importantly, the receptive field is significantly expanded by the global self-attention mechanism of the swin Transformer, allowing the encoding booster to capture the large-scale semantic information effectively and transcend the limitations of CNNs. Furthermore, we integrate the encoding booster in a specially designed U-shaped network through a novel manner, named the Swin Transformer-based Encoding Booster- U-shaped Network (STEB-UNet), to achieve the feature-level fusion of local and large-scale semantics. Remarkably, compared with other Transformer-included networks, the computational complexity and memory requirement of the STEB-UNet are significantly reduced due to the swin design, making the network training much easier. Experimental results show that the STEB-UNet can effectively discriminate and extract buildings of different scales and demonstrate higher accuracy than the state-of-the-art networks on public datasets.

As an important part of electronic warfare, radar countermeasure determines the trend of war to a large extent. Modern radar jamming technology, especially deception jamming technology, plays an increasingly important role. Therefore, how to identify radar deception jamming is very necessary. In this paper, a feature fusion algorithm based on Bayesian decision theory is used to recognize radar deception jamming signals. Firstly, the real echo signal, deception jamming signal (contains range gate pull-off jamming, velocity gate pull-off jamming and range-velocity gate pull-off jamming) and noise signal received by radar are acquired as signal sources. Then bispectrum transformation is used to extract features in several aspects. Finally, kernel density estimation is used to improve the fusion algorithm, and the feature fusion algorithm based on Bayesian decision theory is used to recognize the received signals from radar. Results of the experiment indicate that the algorithm not only can recognize the radar deception jamming, but also has high accuracy.

In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared with state-of-art methods, the proposed method can automatically increase the degree of freedom of the array without further pre-processing on the covariance matrix of array observation data. Moreover, the proposed method can obtain clear spectrum and precise DOAs under harsh estimation environments.

Cloud computing has been envisioned as the next generation architecture of the IT enterprise, but there exist many security problems. A significant problem encountered in the context of cloud storage is whether there exists some potential vulnerabilities towards cloud storage system after introducing third parties. Public verification enables a third party auditor (TPA), on behalf of users who lack the resources and expertise, to verify the integrity of the stored data. Many existing auditing schemes always assume TPA is reliable and independent. This work studies the problem what if certain TPAs are semi‐trusted or even potentially malicious in some situations. Actually, the authors consider the task of allowing such a TPA to involve in the audit scheme. They propose a feedback‐based audit scheme via which users are relaxed from interacting with cloud service provider (CSP) and can check the integrity of stored data by themselves instead of TPA yet. Specifically, TPA generates the feedback through processing the proof from CSP and returns it to user which is yet unforgeable to TPA and checked exclusively by user. Through detailed security and performance analysis, the author's scheme is shown to be more secure and lightweight.

Aiming to address the issue of deception jamming generated by digital radio frequency memory (DRFM), this study proposes a feature extraction algorithm based on variational mode decomposition (VMD) for deception jamming recognition and composite deception jamming recognition. First, models are constructed for the real echo (RE) and the deception jamming signals. Second, the VMD is conducted. Third, the features are extracted from the decomposed intrinsic mode function (IMF) and fed into the support vector machine (SVM) for classification and recognition. To mitigate the challenge of high dimensionality and reduce the complexity of the learning task, mode selection and interclass divisibility of feature selection methods are employed. The effectiveness of the proposed algorithm is verified through simulations. Prior to feature selection, a signal-to-noise ratio (SNR) of 0 dB results in a jamming recognition accuracy exceeding 95%. After feature selection, the recognition accuracy remains largely unchanged, while the recognition speed significantly improves. Compared with other methods in the same field, the recognition accuracy shows a notable improvement. Furthermore, the proposed method is evaluated for its effectiveness in recognizing composite deception jamming, and simulation results validate its performance.

Jamming in defence applications is increasingly difficult because of advanced signal processing countermeasures. The jamming power allocation techniques for MIMO radar are investigated based on two criteria, namely the minimum mean square error (MMSE) for target estimation and the mutual information (MI) between the radar echo and the target impulse response. Different jamming power allocation strategies are obtained using the two criteria, respectively. Furthermore, the robust jamming power allocation strategies are studied when the target, environment or waveform information are not perfectly known. The worst case performance is optimised considering two cases of uncertainty. The analysis indicates that the least favourable sets (LFSs) of the MMSE‐ and MI‐based robust jamming are different if the radar waveform power and the target power spectral density (PSD) lie in the uncertainty sets which are confined by known upper and lower bounds. The LFSs of the MMSE‐ and MI‐based robust jamming are the same if the target PSD and the noise PSD lie in the uncertainty sets. Results are useful for the implementation of cognitive jammer.