IET Signal Processing最新文献

As an effective control measure to ensure uninterrupted power supply to critical loads under extreme faults, network splitting is of great significance for maintaining system safety and stability. The purpose of this study is to develop a method to accurately and quickly find a reasonable splitting surface and reliably perform network splitting. To address the current problem of poor node classification when splitting, the correlation between nodes is obtained through modal analysis of the system. Node classification criteria are proposed to accurately classify different types of nodes and obtain a suitable splitting space. Based on the node correlation, a splitting adaptation index reflecting the suitability of splitting is proposed. Furthermore, a comprehensive index for the optimisation of the splitting surface is proposed by combining the minimum unbalanced power and the splitting adaptation index, and the splitting surface is quickly determined based on this index. Finally, simulation verification is carried out using the IEEE-118 standard system, which shows that the method can accurately determine the splitting space and optimise the selection of the splitting surface.

Distributed optimisation methods are widely applied in many systems where agents cooperate with each other to minimise a sum-type problem over a connected network. An accelerated distributed method based on the inexact model of relative smoothness and strong convexity is introduced by the authors. The authors demonstrate that the proposed method can converge to the optimal solution at the linear rate $��\frac{1}{{�\left(��1�+�1�/��\left(��4�\sqrt{�{\kappa }_g�}��\right)���\right)�}^2}�$ and achieve the optimal gradient computation complexity and the near optimal communication complexity, where κ_g denotes the global condition number. Finally, the numerical experiments are provided to validate the theoretical results and further show the efficacy of the proposed method.

We consider a multiple-input multiple-output (MIMO) channel in the presence of a reconfigurable intelligent surface (RIS). Specifically, our focus is on analysing the spatial multiplexing gains in line-of-sight and low-scattering MIMO channels in the near field. We prove that the channel capacity is achieved by diagonalising the end-to-end transmitter-RIS-receiver channel, and applying the water-filling power allocation to the ordered product of the singular values of the transmitter-RIS and RIS-receiver channels. The obtained capacity-achieving solution requires an RIS with a non-diagonal matrix of reflection coefficients. Under the assumption of nearly-passive RIS, that is, no power amplification is needed at the RIS, the water-filling power allocation is necessary only at the transmitter. We refer to this design of RIS as a linear, nearly-passive, reconfigurable electromagnetic object (EMO). In addition, we introduce a closed-form and low-complexity design for RIS, whose matrix of reflection coefficients is diagonal with unit-modulus entries. The reflection coefficients are given by the product of two focusing functions: one steering the RIS-aided signal towards the mid-point of the MIMO transmitter and one steering the RIS-aided signal towards the mid-point of the MIMO receiver. We prove that this solution is exact in line-of-sight channels under the paraxial setup. With the aid of extensive numerical simulations in line-of-sight (free-space) channels, we show that the proposed approach offers performance (rate and degrees of freedom) close to that obtained by numerically solving non-convex optimization problems at a high computational complexity. Also, we show that it provides performance close to that achieved by the EMO (non-diagonal RIS) in most of the considered case studies.

With oceanic reverberation and a large amount of data being the main sources of interference for underwater acoustic target detection, it is difficult to obtain a more robust detection performance by relying on the traditional constant false alarm rate (CFAR) detection method. An adaptive sonar CFAR detection method based on a back propagation (BP) neural network is proposed. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. The method combines the artificial intelligence algorithm and the traditional detection algorithm, and uses the classification ability of the algorithm to select the detection algorithm, which can effectively improve the adaptation ability of the algorithm and the environment and the false alarm control ability. This method uses a BP neural network to train the target echo signal to complete the clutter background classification and establish the clutter background recognition classification set. According to the output result of each classification, the best CFAR detector is selected from four CA/SO/GO/OS-CFAR detectors to detect the target. The simulation results show the detection performance of the proposed method in a uniform environment, a multi-target environment, and a clutter edge environment. The results show that the environment adaptability is strong for different clutter backgrounds, which further improves the control ability of false alarms under a non-uniform background.

Target detection based on remotely sensed images, which has recently attracted much attention, is a fundamental but challenging task. In remote sensing images, the problem of difficult recognition of small targets or targets with a large aspect ratio arises because the targets have the characteristics of small proportion, dense distribution, and multidirectionality. To address the above problems, this article proposes an improved multiscale feature cross-layer fusion remote sensing target detector based on YOLOv5. First, this method introduces the circular smooth label technique, using YOLOv5 as a rotation detector to solve the angular boundary condition and angle prediction problem for large aspect ratio targets. Second, the explicit visual centre module is introduced to solve the problem of missed detection in target-dense distribution tasks. Finally, a multiscale feature cross-layer fusion structure (S-160) is proposed based on YOLOv5, which improves the detection accuracy of each scale target by fusing shallow and deep feature information and introduces new large-scale features for small target detection to solve the problem that ultrasmall targets in remote sensing images cannot be recognised. Our experiments were conducted on three public remote sensing datasets, DOTA, DIOR-R, and HRSC2016, and the average accuracy (mAP) on the datasets was 76.50%, 70.34%, and 97.68%, respectively, demonstrating the substantial detection performance of the proposed method.

This article proposes a direct position determination (DPD) algorithm for non-circular sources observed by a moving array using the self-calibration technique in the presence of mutual coupling. The method first utilises the symmetric Toeplitz property of uniform linear array matrices with mutual coupling and cyclic Toeplitz property of uniform circular array coupling matrix, realising the decoupled estimations of target position parameters and sensor error parameters. Then the position parameters of multiple non-circular are directly determined based on the subspace data fusion criterion in a decoupled manner, where the subspaces are obtained using the extended array data model with the non-circular properties of the sources. This results in a significant improvement in the accuracy of the target position estimation and the number of distinguishable sources compared to the traditional mutual coupling calibration algorithm. In addition, the theoretical mean square error expression for the position estimations of the proposed algorithm under the influence of finite sampling is derived based on the matrix perturbation analysis theory, and the corresponding Cramér-Rao bound is given. Finally, the correctness of the theoretical derivation and the superiority of the method is verified by simulation experiments.

A first-order finite-state Markov chain (FSMC) typically models the Rayleigh fading channel in the open literature because the first-order FSMC is analytically tractable and can derive closed-form results. Non-orthogonal multiple access (NOMA) has been recognised as a novel wireless technology that addresses challenges in the next generation of mobile communications. According to the power-domain NOMA protocol, channels in the NOMA wireless network are sorted by the channel gain. Then considering NOMA, there is insufficient information on how to further form a suitable model for ordered Rayleigh fading channels based on the first-order FSMC. Given the mathematical statement on how to model the order statistics of multidimensional Markov chains for ordered Rayleigh fading channels, the authors consider these order statistics as a Markov chain, and propose specific processes of representing the state space and constructing the transition probability matrix accordingly. Numerical and simulation results validate the mathematical correctness and accuracy of these novel processes. In addition, for ordered Rayleigh fading channels, the performances of various methods of partitioning the entire signal-to-noise ratio range are compared. The performance comparison results are the same as those obtained for the individual unordered Rayleigh fading channel.

Based on the successful application of generative adversarial network (GAN) models in the field of image generation, this article introduces GANs into the field of deep learning for communication systems and surveys its application in modulation classification. To solve the difficulties in feature extraction, to address the low recognition accuracy of existing radio signal modulation-type recognition methods, and to adapt to complex electromagnetic environments with high noise interference intensity, this article presents a modulation recognition model for high-order digital signals. This model uses the Morlet wavelet transform to analyse time-frequency signals, uses the excellent image generation performance of a GAN model to extract and reconstruct the features of noise-contaminated time-frequency images, and designs an integrated classification network architecture to classify and predict reconstructed images. The experimental results show that the algorithm model proposed in this article can significantly improve the recognition accuracy of high-order digital modulated signals under low signal-to-noise ratio conditions and can achieve 90% recognition accuracy at a signal-to-noise ratio of 1 dB.

Radio Tomographic Imaging (RTI) is a low-cost computational imaging method realised by the Radio Frequency (RF) signal sensing. The target-induced shadowing effect in the RF sensing network is reconstructed as a probability image to estimate the target's position. Then, the RTI-based Device-free Localization (DFL) is becoming a promising research topic in the Location-based Services applications by the Internet of Things (IoT). However, the multipath interference in the RF sensing network often induces the imaging degradation and decreases the DFL accuracy. To deal with the multipath-induced imaging degradation, considering that the target's shadowing occupies a small spatial range in the RF network and expresses some spatial structure, this article explores the spatial correlation in the target's shadowing. Then, a new RTI reconstruction method based on the Structured Sparse Bayesian Learning is proposed to model the spatial correlation implied in the sparse target's shadowing image. Further, the localisation experiments in actual scenes are conducted to validate the utilisation of the spatial correlation in target's shadowing is able to improve the imaging quality of the RTI system by enhancing the robustness towards the multipath-induced imaging degradation.

In many passive localization applications, the reference waveform of the received electromagnetic signals is partly known. The received signals in such scenarios are formulated by modelling the relationship between the received data and the known reference signal waveform and the parameters of interest, such as time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA). By exploiting the prior information carried by the known waveform of the reference signal, the negative impact of random noise can be significantly reduced. Following this guideline, a coherent and an incoherent method is proposed to estimate the TDOA and FDOA parameters between two moving receivers. The Cramer-Row lower bound of the TDOA and FDOA estimation accuracy is also analysed. Simulation results show the advantage of the proposed coherent method in TDOA and FDOA estimation precision over its counterparts, which partially demonstrates that effective exploitation of the known signal waveform can largely improve the performance of TDOA and FDOA estimation.