IET Signal Processing最新文献

Active noise control (ANC) algorithms have been developed within the adaptive algorithm framework. However, multichannel ANC systems, which include numerous reference sensors, control speakers, and error microphones, require a very long control filter converging time for control filter estimation. Traditional system identification methods, such as the Wiener filter method, are better suited for such systems because of their relatively shorter converging time. However, they require large amounts of data to achieve accurate statistical estimation. Therefore, this article proposes a control filter estimation method that requires only a short length of data. An iterative Wiener filter solution using Kronecker product decomposition for multichannel ANC systems converts the filter estimation process by breaking down the extensive control filter into multiple shorter control filters through Kronecker product decomposition. This decomposition effectively reduces the high-dimensional system identification problem into manageable low-dimensional ones. Numerical simulations demonstrate the superiority of the proposed method over conventional Wiener filter techniques, especially in scenarios when limited data are available for control filter estimation.

Data loss is a common problem in intelligent transportation systems (ITSs). And the tensor-based interpolation algorithm has obvious superiority in multidimensional data interpolation. In this paper, a Bayesian robust tensor decomposition method (MBRTF) based on the Markov chain Monte Carlo (MCMC) algorithm is proposed. The underlying low CANDECOMP/PARAFAC (CP) rank tensor captures the global information, and the sparse tensor captures local information (also regarded as anomalous data), which achieves a reliable prediction of missing terms. The low CP rank tensor is modeled by linear interrelationships among multiple latent factors, and the sparsity of the columns on the latent factors is achieved through a hierarchical prior approach, while the sparse tensor is modeled by a hierarchical view of the Student-t distribution. It is a challenge for traditional tensor-based interpolation methods to maintain a stable performance under different missing rates and nonrandom missing (NM) scenarios. The MBRTF algorithm is an effective multiple interpolation algorithm that not only derives unbiased point estimates but also provides a robust method for the uncertainty measures of these missing values.

Tacit understanding refers to the ability of team members to work together seamlessly and intuitively without explicitly communicating in detail. This ability is crucial for effective teamwork in complex situations that involve both manned and unmanned aerial vehicles (UAVs). Existing collaborative tasks between manned and unmanned aircraft focus mainly on optimizing communication and the UAVs’ flight paths but neglect the benefits of tacit and intelligent operational cooperation with pilots. To address this limitation, we propose a tacit collaborative attack method that utilizes the UAVs’ capacity for tacit understanding to infer human intent and select the appropriate targets for collaborative attack missions. A learning framework incorporating intention prediction and reinforcement learning paradigms is developed to teach the UAV to generate corresponding collaborative attack actions. Finally, we present results from extensive simulation experiments in a homemade game environment to demonstrate the efficiency and scalability of our method within the proposed framework. The video can be found at https://www.youtube.com/watch?v=CjXhkD7ko14.

Saliency object detection has been widely used in computer vision tasks such as image understanding, semantic segmentation, and target tracking by mimicking the human visual perceptual system to find the most visually appealing object. The U2Net model has shown good performance in salient object detection (SOD) because of its unique U-shaped residual structure and the U-shaped structural backbone incorporating feature information of different scales. However, in the U-shaped structure, the global semantic information computed from the topmost layer may be gradually interfered by the large amount of local information dilution in the top-down path, and the U-shaped residual structure has insufficient attention to the features in the salient target region of the image and will pass redundant features to the next stage. To address these two shortcomings in the U2Net model, this paper proposes improvements in two aspects: to address the situation that the global semantic information is diluted by local semantic information and the residual U-block (RSU) module pays insufficient attention to the salient regions and redundant features. An attentional gating mechanism is added to filter redundant features in the U-structure backbone. A channel attention (CA) mechanism is introduced to capture important features in the RSU module. The experimental results prove that the method proposed in this paper has higher accuracy compared to the U2Net model.

Reconfigurable intelligent surfaces (RISs) have emerged as a groundbreaking technology, revolutionizing wireless networks with enhanced spectrum and energy efficiency (EE). When integrated with drones, the combination offers ubiquitous deployment services in communication-constrained areas. However, the limited battery life of drones hampers their performance. To address this, we introduce an innovative energy harvesting (EH), that is, EH-RIS. EH-RIS strategically divides passive reflection arrays across geometric space, improving EH and information transformation (IT). Employing a meticulous, exhaustive search algorithm, the resources of the drone-RIS system are dynamically allocated across time and space to maximize harvested energy while ensuring optimal communication quality. Deep reinforcement learning (DRL) is employed to investigate drone-RIS performance by intelligently allocating resources for EH and signal reflection. The results demonstrate the effectiveness of the DRL-based EH-RIS simultaneous wireless information and power transfer (SWIPT) system, demonstrating enhanced drone-RIS spectrum-efficient communication capabilities. Our investigation is summarized in unleashing potential, which shows how DRL and EH-RIS work together to optimize drone-RIS for next-generation wireless networks.

Considering several sources that cause global position system (GPS) interference in civil aviation and the challenges faced by interference recognition algorithms in terms of efficiency and accuracy, we propose an improved You Only Look Once (YOLO)v7-CHS algorithm (YOLOv7-CHS) and investigate its effectiveness in identifying GPS signals and different types of interference signals. First, continuous wavelet transform (CWT) is introduced as a method for processing and analyzing signals in the time–frequency (TF) domain to effectively obtain their temporal and spectral characteristic information. Second, the ConvNeXt structure is integrated into the YOLOv7 backbone network to create a ConvNeXtBlock (CNeB) module to enhance the classification and recognition accuracy of interference signals. Additionally, an attention mechanism is introduced to further improve model recognition accuracy. To effectively improve the capability of signal feature extraction and mitigate the impact of background noise on TF feature suppression, we have integrated the efficient channel attention (ECA) channel attention module with the convolutional block attention module (CBAM) spatial attention module, thereby proposing a hybrid CBAM and ECA (HCE) attention module. Last, to address issues arising from accidental deletion of detection frames and multipath interference negatively affecting model recognition performance, we have employed the soft nonmaximum suppression (Soft-NMS) algorithm while selecting an optimal loss function through comparative analysis. The comparative evaluation experimental results under different circumstances show that YOLOv7-CHS achieves recognition accuracies of 98.0% and 99.6% for various types of signals, respectively. These values represent an increase of 1.7% and 1%, respectively, compared to YOLOv7. Moreover, in terms of lightweight indicators, YOLOv7-CHS exhibits a significant improvement in performance: the frames per second (FPS) is increased by 75.1, the number of parameters (Params) was reduced by 4.75 M, and giga floating point operations per second (GFLOPs) were reduced by 65.9 G while effectively enhancing recognition capabilities. The proposed YOLOv7-CHS not only improves signal recognition accuracy but also reduces model Params and computational complexity, achieving a lightweight model with promising application prospects in the rapid detection and recognition of GPS interference sources in civil aviation.

With the intelligent development of vehicles, the number of vehicles equipped with millimeter-wave (mmWave) radars is increasing, and the possibility of interference between radars is rising dramatically. In automatic driving, it will be common for target detection to be affected by multiple interfering radars. Addressing the mutual interference challenges, an adaptive interference detection method based on support vector machines (SVMs) is proposed. First, a window selection is performed on the received signal and features describing the difference between the normal signal and the interference are extracted. Then, we use a nonlinear SVM to distinguish between the interference and the normal signal. After completing the localization of the interference, we use an autoregressive (AR) prediction model to reconstruct the target echo signal. Results from both multiple interference simulation scenarios and real experimental scenarios show that the accuracy of interference localization and the effect of interference mitigation of the proposed method outperforms the mainstream methods.

Radio map reconstruction is a fundamental problem of great relevance in numerous real-world applications, such as network planning and fingerprint localization. Sampling the complete radio map is prohibitively costly in practice and difficult to achieve. Such methods for reconstructing radio maps from a subset of measurements are now gaining additional attention. In this paper, we first explore the spatial features of signals on the radio map and formulate the reconstruction problem as an optimization problem with feature penalties. Then, we propose an iteration algorithm with spatial feature learning to reconstruct signals on the radio map, which improves the reconstruction accuracy by using an adaptive feature dictionary. Numerical examples are given to demonstrate the viability and performance of our method at last.

In this paper, we propose a low-complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high-mobility scenario, OTFS is becoming a potential scheme for the sixth-generation (6G) wireless communication system. However, the large size of the effective delay-Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low-complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log-linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity.

In this paper, we proposed the distributed quantization and sparsity aware zero attracting least mean square (DQA-ZA-LMS) and its reweighted version (DQA-RZA-LMS) algorithms that can perform sparse spectrum sensing with the lowest power possible. The usage of the quantization aware diffusion adaptive networks has recently been proposed and they can be used in many possible mobile communicative applications. The sparsity aware feature of the proposed algorithm can help the network to track and estimate sparse random vectors that are shown to be the case with the spectrum of the new generation wireless communication systems such as 4G, 5G, 6G, and beyond. The spectrum sensing is considered in this paper to be performed by small cell eNode Bs (SC-eNBs) for the 4^th generation long term evolution (LTE) and the next generation eNB (ng-eNB) networks for the 5^th and 6^th generation mobile communication systems that are scattered in an area collecting distributed quantized data from the environment and working collaboratively to estimate the sparse spectrum vectors. Our findings show that in comparison with the nonquantized version of the distributed ZA-LMS (DZA-LMS) and distributed regularized ZA-LMS (DRZA-LMS) algorithms, our proposed schemes perform considerably well using the quantized data and also reduce power consumption.