IET Signal Processing最新文献

Sidelobe cancellation (SLC) is a well-established beamforming technique for mitigating interference, particularly in the context of satellite communication (SATCOM). However, traditional SLC suffers from the issue of partially canceling the desired signal at high signal-to-noise ratio (SNR), primarily due to unconstrained beamforming processing. Extensive research has been conducted to address this problem; however, existing algorithms have limitations such as dependence on knowledge of signal array vectors or number of interferers and involve high computational complexity. In this paper, we propose a robust SLC algorithm based on beamforming vector norm constraint. Our proposal offers a practical solution by only requiring knowledge of the earth station antenna gain and maximum auxiliary array gain to the desired signal, both of which are fully known. Furthermore, compared to traditional SLC, our proposed method introduces additional computational complexity that only scales linearly with the size of the auxiliary array. Simulation results demonstrate comparable performance between our proposed method and existing techniques such as diagonal loading and spatial degrees-of-freedom control-based algorithms.

Real-time locating systems (RTLSs) suffer from clock synchronization inaccuracy among their distributed reference nodes. Conventional systems require periodic time synchronization and typically necessitate a two-way ranging (TWR) clock synchronization protocol to eliminate their measurement errors. Particularly, frequency-modulated continuous-wave (FMCW) time-based location systems pose unique design considerations on the TWR that have a significant impact on the quality of their measurements. In this paper, a valid operation design diagram is proposed for the case of an FMCW time-based TWR synchronization protocol. The proposed diagram represents an intersection area of two boundary curves that indicate the functionality of the system at a given frequency bandwidth, spectral length, and clock synchronization ambiguity. It presents an intuitive illustration of the measurement’s expected accuracy by indicating a larger intersection area for relaxed design conditions and vice versa. Furthermore, the absence of a working condition can easily be detected before proceeding with the actual system development. To demonstrate the feasibility of the proposed diagram, four scenarios with different design constraints were evaluated in a Monte-Carlo model of a basic TWR system. Moreover, an experimental measurement setup demonstrated the validity of the proposed diagram. Both the simulation and experimental outcomes show that the indicated valid conditions and the distribution of the measurements’ accuracy are in very good agreement.

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis.

Due to the development of digital radio frequency memory (DRFM), active jamming against the main lobe of the radar has become mainstream in electronic warfare. The jamming infiltrates the radar receiver via the main lobe, covering up the target echo information. This greatly affects the detection, tracking, and localization of targets by radar. In this study, we consider jamming suppression based on the independence of RF features. First, two stacked sparse auto-encoders (SSAEs) are built to extract the RF characteristics and signal features carried out by the actual radar signal for subsequent jamming suppression. This method can effectively separate RF features from signal features, making the extracted RF features more efficient and accurate. Then, an SSAE-based jamming suppression auto-encoder (JSAE) is proposed; the mixed signal, including the radar signal, jamming signal, and noise, is input to JSAE for dimensionality reduction. Therefore, the radar signal and RF features, extracted by the two SSAEs in the previous step, are used to constrain the features of the reduced mixed signal. Moreover, we integrate the feature level and signal level to jointly achieve jamming suppression. The original radar signal is used to assist the radar signal reconstructed by the decoder. By first filtering out interference-related features and then reconstructing the signal, we can achieve better jamming suppression performance. Finally, the effectiveness of the proposed method is verified by simulating the actual collected data.

To accelerate the data acquisition speed of magnetic resonance imaging (MRI) and improve the reconstructed MR images’ quality, we propose a parallel MRI reconstruction model (SPIRiT-Net), which combines the iterative self-consistent parallel imaging reconstruction model (SPIRiT) with the cascaded complex convolutional neural networks (CCNNs). More specifically, this model adopts the SPIRiT model for reconstruction in the k-space domain and the cascaded CCNNs with dense connection for reconstruction in the image domain. Meanwhile, this model introduces the data consistency layers for better reconstruction in both the image domain and the k-space domain. The experimental results on two clinical knee datasets as well as the fastMRI brain dataset under different undersampling patterns show that the SPIRiT-Net model achieves better reconstruction performance in terms of visual effect, peak signal-to-noise ratio, and structural similarity over SPIRiT, Deepcomplex, and DONet. It will be beneficial to the diagnosis of clinical medicine.

In the domain of lie detection, a common challenge arises from the dissimilar distributions of training and testing datasets. This causes a model mismatch, leading to a performance decline of the pretrained deep learning model. To solve this problem, we propose a lie detection technique based on a domain adversarial neural network employing a dual-mode state feature. First, a deep learning neural network was used as a feature extractor to isolate speech and facial expression features exhibited by the liars. The data distributions of the source and target domain signals must be aligned. Second, a domain-antagonistic transfer-learning mechanism is introduced to build a neural network. The objective is to facilitate feature migration from the training to the testing domain, that is, the migration of lie-related features from the source to the target domain. This method results in improved lie detection accuracy. Simulations conducted on two professional lying databases with different distributions show the superiority of the detection rate of the proposed method compared to an unimodal feature detection algorithm. The maximum improvement in detection rate was 23.3% compared to the traditional neural network-based detection method. Therefore, the proposed method can learn features unrelated to domain categories, effectively mitigating the problem posed by different distributions in the training and testing of lying data.

In this paper, the Star-Transformer model is improved to obtain more accurate direction of arrivals (DOA) estimation of underwater sonar uniform linear array (ULA) under low signal-to-noise ratio (SNR) conditions. The ideal real covariance matrix is divided into three channels: real part channel, imaginary part channel, and phase channel to obtain more input features. In training, the real covariance matrix is used under different SNRs. In testing, the covariance matrix of samples in the real environment is used as input. The on-grid form is used to estimate the DOA of multiple signal sources, which is modelled as a multilabel classification problem. The results show that the model can be effective and can still have a good DOA estimation performance under the conditions of trained and untrained SNRs, different snapshots, signal power mismatch, different separation angles, signal correlation, and so on. It shows that the model has excellent robustness.

The optical time domain reflectometer (OTDR) curve features of communication fibers exhibit subtle differences among their normal, subhealthy, and faulty operating states, making it challenging for existing machine learning-based fault diagnosis algorithms to extract these minute features. In addition, the OTDR curve field fault data are scarce, and data-driven deep neural network that needs a lot of data training cannot meet the requirements. In response to this issue, this paper proposes a communication fiber state diagnosis model based on fuzzy clustering and an improved ResNet. First, the pretrained residual network (ResNet) is modified by removing the classification layer and retaining the feature extraction layers. A global average pooling (GAP) layer is designed as a replacement for the fully connected layer. Second, fuzzy clustering, instead of the softmax classification layer, is employed in ResNet for its characteristic of requiring no subsequent data training. The improved model requires only a small amount of sample training to optimize the parameters of the GAP layer, thereby accommodating state diagnosis in scenarios with limited data availability. During the diagnosis process, the OTDR curves are input into the network, resulting in 512 features outputted in the GAP layer. These features are used to construct a feature vector matrix, and a dynamic clustering graph is formed using fuzzy clustering to realize the fiber state diagnosis. Through on-site data detection and validation, it has been demonstrated that the improved ResNet can effectively identify the full cycle of fiber states.

Motion artifacts are a major challenge in the in vivo application of catheter-based cardiac imaging modalities. Gating is a critical tool for suppressing motion artifacts. Electrocardiogram (ECG) gating requires a trigger device or synchronous ECG recordings for retrospective analysis. Existing retrospective software gating methods extract gating signals through separate steps based on changes in vessel morphology or image features, which require a high computational cost and are prone to error accumulation. In this paper, we report on an end-to-end unsupervised learning framework for retrospective image-based gating (IBG) of catheter-based intracoronary images, named IBG Network. It establishes a direct mapping from a continuously acquired image sequence to a gated subsequence. The network was trained on clinical data sets in an unsupervised manner, addressing the difficulty of obtaining the gold standard in deep learning-based motion suppression techniques. Experimental results of in vivo intravascular ultrasound and optical coherence tomography sequences show that the proposed method has better performance in terms of motion artifact suppression and processing efficiency compared with the state-of-the-art nonlearning signal-based and IBG methods.

In this paper, a power allocation algorithm of dual-function radar–communication system with limited power is proposed to obtain better overall system performance measured by the weighted summation of radar signal to interference plus noise ratio (SINR) and communication channel capacity. First, a power allocation model is established to maximize the radar SINR and communication channel capacity with limited transmitted power. Then, the Karush–Kuhn–Tucker (KKT) conditions are used to solve the optimal objective function under the condition that only radar SINR or communication channel capacity is considered, respectively. Finally, the optimal value is combined with the original model and transformed into a single objective optimization model, and the optimal power is obtained by solving the model through the iterative optimization algorithm. Simulation results show that, compared with other power allocation algorithms, the proposed algorithm can achieve better radar-communication integration performance under the same transmit power.