Satellite communication links leveraging multi-source physical layer features provide critical authentication technology for secure satellite networks. However, existing physical layer security schemes suffer two major limitations: i) most methods perform one-shot decisions without tracking trust evolution over changing link geometries; ii) fixed decision rules fail to accumulate evidence and degrade robustness when spoofing parameters adapt. We propose a multi-stage dynamic trust authentication framework. Firstly, we introduce a multi-feature statistical detection module that performs real-time preliminary authentication based on Doppler shifts and power residual analysis. Then we design a particle-filtering-based trust-state estimation mechanism that fuses historical credibility with new observations via adaptive resampling, enabling continuous trust evolution and swift anomaly response. Experiments on STK-simulated datasets demonstrate that the proposed method increases secure throughput by 5.5% and reduces false-alarm and missed-detection rates by 27% and 29%, respectively.

Cooperative path planning for UAVs using hierarchical optimal control is a critical technology for urban emergency communication networks. Existing methods typically decouple trajectory optimization from communication scheduling, which inherently leads to suboptimal performance. Attempting to solve the problem monolithically creates a large-scale Mixed-Integer Non-Linear Program that is computationally intractable for real-time deployment. Compounding this, current models often overlook the need for dynamic UAV role-switching, limiting the system’s functional flexibility and operational adaptability. We propose a hierarchical control framework that decomposes the problem into a dual-layer model predictive control architecture. Specifically, we linearize the original problem within a receding horizon control loop for long-term task and trajectory planning in the strategic layer. Guided by this, the lower trajectory layer employs non-linear model predictive control, solved via sequential convex programming, to generate real-time trajectories and perform adaptive role switching. Simulation results demonstrate that the proposed framework reduces mission makespan by 8.6% and increases the total data collected by 2.4%, validating its efficiency and robustness.

Specific emitter identification (SEI) is a security authentication technology by utilizing radio frequency fingerprinting (RFF) features. However, current mainstream RFF feature extraction methods based on neural networks (NNs) generally suffer from poor interpretability and difficulties in architecture optimization. To address these issues, we propose a novel convolution-enhanced swin vision transformer (CoSwinVIT) that combines filter characteristics of NN architectures to achieve a uniform spectrum response. Specifically, we treat the NN used in SEI tasks as filters and investigate their filter characteristics by applying Fourier transforms to hidden vectors. Through spectral analysis, we found that different NN architectures exhibit significantly different responses to the signal spectrum. This affects the model’s sensitivity to specific frequency bands of the signal, thereby influencing its accuracy (Acc). Subsequently, by integrating the filtering properties of swin vision transformer (SwinVIT) and convolutional neural network (CNN), we achieve a uniform spectral response design. Finally, to evaluate the performance of the CoSwinVIT architecture, we design both a supervised learning algorithm and a contrastive learning-based self-supervised algorithm. Experimental results on a real-world automatic dependent surveillance-broadcast (ADS-B) and wireless fidelity (WiFi) dataset indicate that CoSwinVIT provides a more uniform spectrum response. Under supervised learning, the proposed CoSwinVIT obtains the accuracies of 98.6% and 99.8% on the ADS-B and WiFi datasets, respectively. Under self-supervised learning with 5% and 10% labeled data, the accuracies of 73.6% and 86.3% are achieved on the ADS-B dataset, and the accuracies of 81.6% and 91.5% are achieved on the WiFi dataset. These results surpass the state-of-the-art (SOTA) methods used in the SEI tasks.

Deep learning has demonstrated promising results over traditional hand-crafted methods for automatic modulation classification (AMC), which plays a critical role as an intermediate step between signal detection and modulation. However, acquiring large-scale labeled data remains challenging, as both data quality and annotation costs are critical factors in achieving accurate and efficient training. In this paper, we propose a novel self-supervised contrastive learning (SSCL) with gradient-adversarial-based data augmentation (GADA) approach for AMC. Additionally, a meticulous encoder based on Transformer-LSTM architectures is employed to pre-train a feature extractor using unlabeled base classes. Subsequently, knowledge transfer is employed to fine-tune the feature extractor, and ensemble learning is introduced to efficiently leverage multiple classifiers for joint decision-making. Experiments demonstrate that we achieve up to 91.87% accuracy on the challenging large-scale RadioML2018.10a dataset, demonstrating performance competitive with state-of-the-art supervised implementations.

Specific Emitter Identification (SEI) is a non-encrypted authentication technology that adds a layer of security to wireless communications in intelligent transportation systems. However, most existing SEI methods are constrained to identify fixed classes and cannot learn incrementally. In practical applications, the constant emergence of new classes or tasks necessitates the capacity to continuously learn new classes from streaming data. In this paper, combining momentum-based prototype correction and hierarchical regularization, a simple and effective non-example class-incremental method for SEI is proposed, named MoPC-HR. The momentum-based prototype correction dynamically adjusts old class prototypes (i.e., old class centers), strengthening the separation between old and new classes and enabling smoother integration of new classes. Hierarchical regularization is applied at multiple levels to control feature differences between old and new classes, preventing the model from favoring new classes and reducing catastrophic forgetting. Experiments on real-world AIS-100 and ADS-B datasets show that MoPC-HR outperforms state-of-the-art methods. Specifically, in the incremental phase 20, MoPC-HR achieves an average accuracy of 97.04%, average forgetting of 2.95%, and average intransigence of 2.85% on the AIS-100 dataset; and an average accuracy of 95.99%, average forgetting of 3.66%, and average intransigence of 3.60% on the ADS-B dataset. The code is available at https://github.com/xmuLdz/MoPC-HR.git

Recently, one-bit direction of arrival (DOA) estimation has received significant attention due to its low cost and low implementation complexity, while still achieving high accuracy without the need of high-resolution measurements. In this work, we consider nonlinear estimation errors under finite number of snapshots in one-bit covariance reconstruction, and propose the two-step reconstruction approach, first using the arcsine law to reconstruct the unquantized covariance matrix and then incorporating the Toeplitz Hermitian structure as prior information to reconstruct the full-scale virtual uniform linear array (ULA) covariance matrix. It is shown that the error is smaller than the case when the two steps are swapped in order. Simulation results demonstrate the large difference in performance due to the order in which the two steps are applied, our proposed method has remarkably outperformed the current state-of-the-art solutions.

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.

Traditional imaging algorithm of multireceiver synthetic aperture sonar based on Loffeld’s bistatic formula (LBF) suffers from a tradeoff between focusing performance and efficiency due to the spatial variance of LBF. To improve the performance and efficiency, we develop a chirp scaling (CS) algorithm based on the reformulated LBF, which includes the range-variant and range-invariant terms. The phase error caused by LBF reformulation and range-invariant term are first compensated. Since the range-variant term used for the design of the CS algorithm is weighted by a factor, all filter functions of CS algorithm are newly deduced. Based on these improvements, the sub-block width is allowed to be enlarged. Consequently, both the efficiency and performance are improved. Numerical results show that the ghost suppression performance of our method is improved nearly 10 dB compared to traditional method. Besides, our method is more time-saving than traditional method.

In this letter, we consider the problem of joint delay-Doppler shift estimation of multiple moving targets in a passive radar system using orthogonal frequency-division multiplexing communication signals. An atomic norm-based algorithm is proposed for better estimation performance and higher robustness against the demodulation error. We utilize a new atomic set with the unknown demodulation error from the direct-path signal included, aiming to reduce the impact of high symbol error rate (SER), in contrast to previous works that introduce$\ell _{1}$-norm to exploit the sparsity of the afore-mentioned error. By converting to a semidefinite program, the problem is solved under the convex optimization framework. Simulation results show that the proposed method performs better under high SER conditions.

Classic multireceiver synthetic sonar (SAS) reconstruction algorithms relying upon Loffeld’s bistatic formula (LBF) should firstly utilize the data segmentation approach to remove the multireceiver deformation (MD) term. Usually, the small sub-block is required by traditional LBF based imaging algorithms to produce the high performance result. To address this issue, we describe a receiver-by-receiver based imaging algorithm. The presented method firstly reformulates the original LBF to range dependent and independent terms. Then, the image reconstruction receiver-by-receiver is carried out. The LBF reformulation and the quadratic expansion of range-dependent term needed by subsequent range-Doppler (RD) algorithm would generate approximation error corrected by data segmentation approach in frequency domain. After performing the range cell migration correction (RCMC) receiver-by-receiver, the signal in range-Doppler domain related to each receiver is coherently synthesized, and the azimuth offset is thereafter corrected. The focusing result would be produced when the azimuth inverse Fourier transform (IFT) is completed. On the basis of simulation and real data test experiments, conventional focusing approach with wide sub-block is seriously affected by ghost targets, which are successfully avoided by presented method with the same wide sub-block. It indicates that the presented method relaxes the requirement for the sub-block width.

Specific emitter identification (SEI) is a non-cryptographic authentication technique to provide an extra security layer for wireless devices, which has promising applications. However, the traditional methods of SEI are only available in limited equipments. In actual application scenarios, new devices (as new classes) are constantly appearing. In this paper, an effective class incremental learning (CIL) method is proposed for SEI, named class-incremental with self-training and prototype augmentation (CISP). It is a teacher-student network. Firstly, the teacher network trained by the old-class data is utilized to instruct the student network to adapt the new classes while retaining the old-class knowledge through the knowledge distillation (KD) techniques. Secondly, in order to mitigate the problem of favoring the new classes, weight aligning (WA) method is introduced to balance the weights of the new-class and old-class classification layers in the student network. Lastly, the old-class samples are recalled from the unlabeled dataset by the student network and input into the teacher network. Then the feature prototypes of the old classes are constructed and augmented. This would further ease the imbalance between the old and new classes and alleviate the problem of noisy pseudo-labels. Experiment results on the real AIS-100 dataset and ADS-B-100 dataset with the number of the initial classes being 20 and 20 classes per incremental step demonstrate that the proposed method can achieve an average accuracy of 95.29% and 95.84%, respectively. It effectively mitigates the catastrophic forgetting of the model and is superior to the state-of-the-art incremental learning approaches of not saving the old-class samples.

Underwater acoustic (UWA) channels present diverse sparsity, which is challenging for UWA communications and underwater networked applications. Conventional methods fail to adapt to the uncertain sparsity due to inflexible adjustment rules or improper assumptions. This paper investigates the estimation of channels with uncertain sparsity and model-driven deep learning (DL) based estimation networks are proposed. We construct the channel estimation as sparse signal recovery model and exploit the channel sparsity based on the approximate message passing (AMP) method. Then the learned AMP network with soft-thresholding shrinkage function (ST-LAMP) is designed as a generalized estimator and suitable to random sparse UWA channels. Moreover, to achieve the Bayesian optimal solution, Gaussian mixture (GM) prior is imposed for UWA channels and the GM-LAMP network is formulated with the derived shrinkage function based on the minimum mean squared error criterion. The proposed networks incorporate the learning ability of DL methods by exploiting marine data and the domain knowledge from the classical model to realize parameter configuration and adaptive optimization. Experimental results verify that the proposed networks outperform benchmarks over UWA channels with diverse sparsity in terms of adaptability and effectiveness. Especially, real tests present the feasibility of proposed networks for on-line deployments.

Two-dimensional Direction of arrival (2D DOA) is a challenging problem in sparse circular arrays (SCAs), especially in the field of underdetermined estimation. In this paper, based on sparse signal recovery (SSR) theory, an underdetermined 2D DOA estimation method is proposed for SCAs. The Khatri-Rao (KR) subspace method is introduced to construct a signal model for underdetermined DOA estimation. Then, different from the previous method of converting 2D discrete grids into 1D vectors, a new 2D SSR model based on a 2D over-complete basis is constructed. Finally, an improved algorithm based on the compressed sampling matching pursuit algorithm (CoSaMP) is used to realize SSR and implement underdetermined 2D DOA estimation. Numerical simulation results indicate that the proposed method has excellent DOA estimation accuracy with extremely low complexity.

The automatic identification system (AIS) is the automatic tracking system for automatic traffic control and collision avoidance services, which plays an important role in maritime traffic safety. However, it faces a possible security threat when the maritime mobile service identity (MMSI) that specifies the vessels’ identity in AIS is illegally counterfeited. To guarantee the communication security of AIS for preventing fraudulent devices, we design a novel lightweight Transformer-based network GLFormer for specific emitter identification (SEI) to provide an extra security layer for AIS terminal emitters. Concretely, the gated local attention unit (GLAU) and the gated sliding local attention unit (GSLAU) modules that combine a simplified gated attention unit (GAU) and a sliding local self-attention (SLA) are developed in GLFormer to extract the radio frequency fingerprint (RFF) features automatically from the raw in-phase signals. Especially, the simplified GAU focuses on more critical RFF features and filters out the irrelevant information from the raw signal to improve performance, which is also a single-head self-attention module with fewer parameters for lightweight. Meanwhile, the SLA limits self-attention operation to a window, introducing the inductive bias of local information to enhance performance further and reducing the quadratic computational complexity to linearity for efficiency. Experimental results demonstrate that the GLFormer achieves 96.31% and 89.38% identification accuracy in the constructed AIS transient and AIS steady-state datasets with 50 vessels, respectively. The 99.90% identification accuracy is achieved in the universal software radio peripheral (USRP) dataset with ten devices. It is not only better than the existing methods but requires much fewer parameters and lower computational complexity; besides, it is also suitable for working with long signal sequences.

In this letter, a novel relocating enhanced nested array (RENA) configuration is proposed. Compared with most existing sparse array configurations, the proposed RENA has a hole-free difference co-array, simple closed expressions for the array geometry and degrees of freedom (DOFs), and also achieves more consecutive DOFs. Based on the above good properties of the proposed RENA, we improve a root multi-signal classification algorithm based on pseudo-noise subspace (PNS-root-MUSIC) for direction of arrival (DOA) estimation. The PNS-root-MUSIC algorithm has lower algorithm complexity due to no exhaustive spectral peak search, and takes full advantage of the larger hole-free co-array of the proposed RENA, yielding a higher accuracy of DOA estimation. The results of theoretical analysis and simulations demonstrate the superior performance of the proposed RENA. The simulation results show that the improved PNS-root-MUSIC algorithm has better DOA estimation performance compared with that of existing algorithms.

Illegally forged signals in automatic identification system (AIS) pose a threat to maritime traffic safety management. In this paper, a multi-level sparse representation based identification (MSRI) algorithm is proposed for specific emitter identification (SEI) in the AIS. The MSRI innovatively combines neural networks with sparse representation based classification (SRC). Channel attention mechanism is introduced to a multi-scale convolutional neural network (CNN) for extracting hidden features in the signal. These extracted features are divided into shallow and deep features according to the depth of the network layer they are extracted from. The original AIS signals and the two-level features are spliced together to form a multi-level dictionary. Subsequently, a sparse representation based identification is performed on the decorrelated multi-level dictionary using the principal components analysis (PCA) method. The proposed MSRI is evaluated on a dataset composed of real-world AIS signals, and compared with the state-of-the-art identification algorithms. The evaluation is based on several factors including computational complexity, number of training samples, and number of emitters. Numerical results indicate that the proposed algorithm can identify emitters with higher accuracy and requires lower training time compared to other methods. Given more than 15 training samples at each emitter, the MSRI can identify nine emitters with an accuracy higher than 90%.

In this article, a wideband Direction-of-Arrival (DOA) estimation method for underdetermined scenarios is proposed, which effectively solves the basis mismatch problem. Based on the fractional Fourier transform (FRFT), the wideband received signal model with a coprime array is first derived by exploiting the aggregation characteristic of wideband linear frequency modulated (LFM) signals in the fractional Fourier (FRF) domain. Then, in order to increase the degree of freedom, an extended uniform linear array is built, and the covariance matrix of the signal is reconstructed by employing the penalized atomic norm minimization with the consecutive spatial dictionary. Meanwhile, without the knowledge of the noise level, the noise variance is estimated from the noisy incomplete data, which is utilized to improve the covariance matrix reconstruction performance. Additionally, for the unconditional model, the Cramér-Rao bound for the wideband DOA estimation based on a coprime array is derived. Different from the existing methods, the proposed method not only can estimate more DOAs of wideband signals than the number of physical sensors in the presence of unknown amplitude distortion but also can obtain more accurate DOA estimation performance without basis mismatch. The effectiveness of the proposed method is verified by our numerical results.

The novel time-frequency transform, called synchro-compensating chirplet transform, is proposed to represent optimally the overlapping signals in a time-frequency domain. By the control of the self-tuning demodulated operator, an instantaneous rotating operator is introduced in the proposed algorithm to blur the noise and compensate the energy of each component simultaneously. The effectiveness of the method for micro-Doppler signals with a high noise is validated through examples.