SAR ship detection faces challenges such as scale diversity, weak target features, and strong background interference. To address these issues, we propose CSCF-Net, integrating a Multi-Scale Feature Fusion (MSFF) module for cross-scale contextual aggregation and a Multi-Task Interactive Detection Head (MTIDH) for task-specific optimization through dynamic deformable convolution. Extensive experiments on three SAR datasets demonstrate superior performance: CSCF-Net achieves 98.5% mAP on SSDD, 91.5% on HRSID, and 98.1% on SAR-Ship-Dataset, with 1.0%, 2.2%, and 0.9% improvements over baseline respectively, outperforming state-of-the-art methods and validating the effectiveness of our proposed method.

Orthogonal Time Frequency Space (OTFS) modulation can effectively support high-mobility communication scenarios. However, Orthogonal Frequency Division Multiplexing (OFDM) based OTFS suffers from high spectrum leakage. Implementing channel estimation and equalization that simultaneously supports both OTFS and OFDM also faces challenges. In this paper, we adopt Filter Bank Multi-Carrier (FBMC) modulation as an alternative to OFDM and propose a TF-domain pilot-aided channel estimation and equalization scheme, which can improve spectrum leakage and enhance compatibility. Specifically, first, based on the core function of the prototype filter, we choose the Hermite prototype filter with symmetric properties to construct the FBMC-based OTFS system, enhancing adaptability to dynamic channels. Second, considering the dynamic characteristics of fast time-varying channels, we construct Bessel fitting or priori information-assisted channel extrapolation mechanisms to achieve accurate tracking of channel parameters. Finally, we derive the criterion for determining the channel wide-sense stationarity time interval, which provides a basis for the update mechanism of prior information. Simulation results show that the proposed scheme can work robustly on doubly-selection channels. Compared to classical OTFS, FBMC-based OTFS significantly improves reliability in high mobility scenarios.

Translating the complex dynamics of biological nervous systems into engineered models is crucial for understanding the essence of intelligence and creating human-like artificial intelligence. This article presents a four-dimensional memristive Morris-Lecar model, constructed by coupling a locally active memristor (LAM) with the Morris-Lecar model, which features fast-slow dynamics. Through stability analysis of equilibrium points, the potential mechanism is qualitatively investigated by which three types of equilibrium points trigger neuronal oscillatory activity. Different dimensions of bifurcation diagrams and other numerical techniques reveal that neuronal firing activity and its dynamic characteristics are regulated by three factors: 1) LAM; 2) the slow variable; and 3) ion channels. Based on bursting activity, the fast-slow variable analysis method is employed to study fold and Hopf bifurcations, and the fast-slow dynamics under synergistic control are elucidated. Notably, coexisting attractors are discovered and found to be closely related to LAM. Finally, the neuronal model is implemented on an FPGA to generate firing activities with diverse dynamic characteristics.

Severe Doppler shift and the obstruction of the Line-of-Sight (LoS) path significantly decrease the communication performance. This article considers a downlink channel estimation problem for reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple-input-multiple-output nonorthogonal multiple access (MIMO-NOMA) system in Internet of Vehicles (IoV) with high-mobility scenarios. By introducing the concept of aggregated slot and half slot, a 5G subframe partitioning scheme without changing the standard 5G frame structure is first proposed to facilitate the formulation of the received signal. Then, the received signal is modeled as a quadrilinear tensor, meeting with a canonical polyadic decomposition (CPD) form, which separates Angle of Arrival (AoA), Angle of Departure (AoD), time delay, and Doppler shift into four corresponding factor matrices and avoids parameters coupling. Subsequently, by leveraging the Vandermonde structure of the factor matrix and the low-rank property of the mmWave channel, we design a four-dimension (4-D) structured tensor decomposition-based method to decompose the tensor into four factor matrices in a closed-form solution, which can avoid initialization and iteration. Accordingly, the channel parameters can be extracted by a simple correlation-based estimator. After obtaining the channel parameters, we construct a least squares problem to obtain the channel path gain, which can avoid solving the scaling matrix. Finally, numerical experiments are conducted to confirm the effectiveness of the proposed algorithm, in which the Cramér-Rao bound (CRB) results for channel parameters are derived as the benchmark.

Infrared dim and small target detection algorithms play a crucial role in military applications. To address the issues of high computational complexity, poor interpretability, and insufficient utilization of image information—which often result in inefficient model convergence and incomplete target retention—this paper proposes a novel robust Column-Row (CUR)-based matrix decomposition method. First, we construct a novel sampling strategy to reconstruct the original image through analysis of target and background characteristics, establishing a foundation for matrix decomposition. Subsequently, based on the reconstructed images, we propose a new CUR-based matrix solution method to solve the low-rank terms in the infrared dim target detection model, effectively reducing computational complexity. Finally, we employ Randomized Block Krylov Methods as a replacement for traditional Singular Value Decomposition (SVD) to further enhance computational efficiency. The visual detection results of each scene and various evaluation indexes such as Receiver Operating Characteristic (ROC) curve show that the proposed method has satisfactory computational efficiency and detection performance.

Due to the nonstationary, time-varying, and target-like characteristics of sea clutter, detection of small targets embedded within it has been a long-standing and formidable challenge in the field of remote sensing. Traditional adaptive detectors based on sea clutter modeling often struggle to achieve satisfactory detection performance. To address this issue, this letter proposed a novel small target detector embedded in sea clutter that leverages dual-polarization correlation features and one-class classifier. Initially, three distinct features are extracted from the significant differences between the target echoes and sea clutter in the dual-polarization correlation domain. Each individual feature possesses a certain level of discriminative power. Furthermore, under the framework of anomaly detection, a one-class classifier for small sea surface target detection is constructed based on the fast convex hull learning (FCHL) algorithm to make the final decisions. Experimental results based on the IPIX datasets demonstrate that the proposed detector outperforms several existing detectors.

In complex electromagnetic environments, the deinterleaving of dense, interwoven radar pulse signals poses a formidable challenge. To address the propensity of conventional graph models for confusion and misclassification in such scenarios, this letter proposes a novel method for radar emitter signal deinterleaving: the Multi-view Attention Hypergraph Neural Network (MVA-HGNN). This model maps radar pulses to nodes in a hypergraph, leveraging hyperedges to capture higher-order correlations among pulses. This overcomes the limitation of traditional graphs, which can only describe pairwise relationships. To fully exploit the heterogeneous information within Pulse Descriptor Words (PDWs), we construct two distinct hypergraph views: ”spatial” and ”intrinsic.” In the MVA - HGNN model, parallel hypergraph network branches learn node representations from different views. An advanced attention - based fusion mechanism is introduced to dynamically integrate these feature representations. Simulation results demonstrate that our method achieves superior performance, particularly in small data scenarios, showing great potential for engineering applications.

Ultra-wideband (UWB)-basedindoor positioning systems (IPS) have superior positioning performance. In indoor complex environments, non-line-of-sight (NLOS) reception due to multipath and signal fading can significantly affect the ranging accuracy of UWB signals. In this case, conventional machine learning (ML) and deep learning (DL) methods often fail to achieve the required accuracy and precision. To address this challenge, we propose an ADCNN-BiLSTM-Transformer model that incorporates adaptive dilated convolution, adaptive weight decay, and an improved transformer-like attention mechanism to effectively classify NLOS signals in UWB localization. Experimental results show that the model achieves 92.19% accuracy on the public eWINE dataset, outperforming existing methods by 7.81%. It also achieves a 3.74% improvement on the public UPT dataset.

Millimeterwave (mmWave) techniques will be a key enabler for wireless communications to achieve high data rates. Additionally, Filter Bank Multi-Carrier (FBMC) with good spectral properties has also been regarded as an important transmission technique for future wireless communications. In this letter, we design and analyze an FBMC-based mmWave Multiple-input Multiple-output (MIMO) system. Specifically, we first pre-code quadrature amplitude modulation symbols in time to ensure that the MIMO technique becomes simple in FBMC. Secondly, we determine the optimal subcarrier spacing by maximizing the signal-to-interference ratio. Finally, using a lens antenna array combined with a simple channel estimator, we transmit data to the receiver. Simulation results show that FBMC can effectively support multi-antenna and mmWave techniques, providing favorable efficiency and reliability. Furthermore, we also verify that Alamouti's space time block code can provide considerable diversity gain.

Earth-based radar (EBR) is an important type of remote-sensing instrument for planetary observation. EBR takes advantages in large-scale imaging swath, high repeatability, great flexibility,etc. The upcoming 233 MHz-frequency European Incoherent Scatter Scientific Association (EISCAT) 3D radar system will provide important features to lunar observation as introduced in this study. EISCAT_3D (E3D) radar is a powerful multi-static radar system. The 1.3 m-wavelength wave of E3D can penetrate deeper, about 30 m below lunar average surface which can reach the second layer, i.e. layer of ejecta. E3D radar supports dual/quadrature polarimetry, which gives it good flexibility and lower ambiguity in the inference of scatter’s properties. Also, there is less ambiguity in scattering regimes between icy and non-icy scatters for 1.3 m wavelength than for shorter wavelengths as given from the simulation results. Besides, the high topographic resolution (which requires forming interferometric baselines with distant telescope antennas) of E3D radar along with its penetration depth makes it possible for detection of sublunarean cavities by signatures of depression. As a whole, the 1.3 m-wavelength three-dimension (3-D) polarimetric imaging of the Moon by E3D radar, on a spatial resolution of about 200 m, will be valuable for obtaining new information about the geology and subsurface structure of the Moon, and can be used in search of buried water ice and sublunarean cavity,etc. Furthermore, we envision the collaboration of E3D radar with large telescope antennas in China, including Daocheng Solar Radio Telescope (DSRT) and Mingantu spectral radioheliograph (MUSER) for better imaging ability and detectivity.

Radar signal deinterleaving is an essential step in perceiving the battlefield situation and mastering military initiative in the information battlefield. Complex radar systems are rapidly updated and iterated, which exacerbates the possibility of “increasing batch” and “mistaken batch” during radar signal deinterleaving. In this letter, a novel method based on complex networks and Laplacian graph clustering is proposed to improve the accuracy of deinterleaving. First, a complex network is constructed to mine the spatial correlation relationships of the same radar signals. Then, based on the graph characteristics of the Laplacian matrix, the number of cluster centers is solved. Finally, this letter employs Laplacian spectral clustering based on graph segmentation to accomplish radar signal deinterleaving. The results of the experimental simulation demonstrate that the method is capable of effectively tackling the “increasing batch” and “mistaken batch” problems of radar signal deinterleaving, and could reach 99.88% deinterleaving accuracy with high robustness.

In this article, we address the time-of-arrival (TOA)-based source localization problem in mixed light-of-sight (LOS)/non-LOS (NLOS) environments, where localization accuracy is degraded by both measurement noise and NLOS error. A novel exponential optimization problem is formulated based on new minimum joint error entropy criteria and statistical characteristics of measurement noise. After that, a two-step relaxation method is proposed. In the first step, the original problem is relaxed into a nonexponential problem which maintains the consistency of the solution. The second step is to transform the nonexponential problem into a convex problem. Furthermore, we extend the method to asynchronous networks where the source and anchors are not time synchronized. Numerical results show that the proposed method can provide significant robust performance in synchronous or asynchronous networks, whether in sparse or dense NLOS scenarios.

The performance of existing maneuvering target tracking methods for highly maneuvering targets in cluttered environments is unsatisfactory. This paper proposes a hybrid-driven approach for tracking multiple highly maneuvering targets, leveraging the advantages of both data-driven and model-based algorithms. The time-varying constant velocity model is integrated into the Gaussian process (GP) of online learning to improve the performance of GP prediction. This integration is further combined with a generalized probabilistic data association algorithm to realize multi-target tracking. Through the simulations, it has been demonstrated that the hybrid-driven approach exhibits significant performance improvements in comparison with widely used algorithms such as the interactive multi-model method and the data-driven GP motion tracker.

This article develops a robust source localization method using time delay and Doppler shift measurements, where the sensor motion effect accompanied by sensor location errors cannot be ignored. We begin by transforming the time delay and Doppler shift measurement models into a series of nonlinear equations that take sensor location errors into account, and then construct a constrained weighted least squares (CWLS) problem based on these equations. Because of the nonconvex nature of the problem, we relax it into a semidefinite programming (SDP) problem via convex relaxation and further propose a scheme to eliminate the influence of the additional estimation bias caused by the approximation applied in the transformation of measurement models. To perform the bias reduction, we derive the theoretical expression of the solution bias and then subtract it to obtain a bias-reduced solution. We also derive the closed-form expression of the hybrid Cramer–Rao lower bound (HCRLB) as the performance benchmark. Theoretical analysis and simulation results demonstrate that the mean-square error (MSE) performance of the proposed method can achieve the HCRLB accuracy and the bias can be significantly reduced with bias reduction.

A novel algorithm that combines the generalized labeled multi-Bernoulli (GLMB) filter with signal features of the unknown emitter is proposed in this paper. In complex electromagnetic environments, emitter features (EFs) are often unknown and time-varying. Aiming at the unknown feature problem, we propose a method for identifying EFs based on dynamic clustering of data fields. Because EFs are time-varying and the probability distribution is unknown, an improved fuzzy C-means algorithm is proposed to calculate the correlation coefficients between the target and measurements, to approximate the EF likelihood function. On this basis, the EF likelihood function is integrated into the recursive GLMB filter process to obtain the new prediction and update equations. Simulation results show that the proposed method can improve the tracking performance of multiple targets, especially in heavy clutter environments.

Space-time adaptive processing requires reliable training samples to support clutter covariance matrix calculation of the cell under test in the process of moving target detection. However, the heterogeneous clutter and the existence of outliers in the training samples violate the assumption of independent identically distributed with the cell under test, which is crucial for statistical estimation. To solve this problem, this letter suggests a new strategy to select training samples that are homogeneous with the cell under test. First, the generalized inner product method is used to filter training samples with outliers and generate the corresponding load matrix. Second, a distance metric between the training sample clutter subspace and the cell under test clutter subspace is formed by the volume cross-correlation function, the distance metric is used to judge the clutter similarity between the training sample and the cell under test. Finally, the training samples with similar clutter distribution characteristics to the cell under test are selected. Experimental results show that Space-time adaptive processing performance can be improved effectively, especially in heterogeneous environments.