Search Results (78)

In this work, we explore resilient cooperative localisation for electric vehicles subject to the hybrid attack of gradual global navigation satellite system (GNSS) drag-off spoofing along with received signal strength indicator (RSSI) jamming. In order to mitigate such attacks, a deep learning-based physical-layer security approach is presented. The presented approach includes a long short-term memory (LSTM) detector for attack detection, a regression-based RSSI signal purifier, and a cooperative fusion scheme, which decreases the dependence on the GNSS branch in case of attack detection. The proposed approach is validated via the Berlin Vehicle-to-Everything (V2X) dataset with respect to six scenarios, including benign GNSS-only and cooperative localisation, attacked localisation without defence, and attacked localisation with physical-layer security support. According to the experimental evaluation results, the considered hybrid attack significantly impacts the localisation accuracy, leading to an increase in the GNSS-only localisation error to root mean square error (RMSE) = 149.93 m, mean absolute error (MAE) = 129.81 m, and maximum error = 259.62 m. At the same time, the proposed cooperative localisation with physical-layer security decreases the attacked cooperative localisation error to RMSE = 4.00 m, MAE = 3.51 m, and maximum error = 12.01 m. Full article

In this paper, we investigate secrecy energy efficiency (SEE) maximization in a rate-splitting multiple access (RSMA)-enabled UAV communication system, which consists of a communication UAV serving legitimate ground users (GUs) and a cooperative jamming UAV transmitting jamming signals to degrade the channel of the eavesdropper (Eve). Taking into account the propulsion energy consumption of fixed-wing UAVs, we formulate a non-convex SEE maximization problem by jointly optimizing communication scheduling, CUAV transmit power, and the trajectories of both UAVs. To tackle the non-convex problem, an iterative optimization algorithm combined with the Dinkelbach method and successive convex approximation (SCA) is developed to obtain a suboptimal solution. Simulation results demonstrate the convergence of the proposed algorithm and show the proposed joint optimization scheme significantly improves SEE compared with benchmark schemes. Full article

Timely situational awareness is essential in disaster management but normal Unmanned Aerial Vehicle (UAV) flight cannot take place when the Global Positioning System (GPS) signals are blocked or jammed. This paper addresses the issue of swarm cohesion and localization in these hostile conditions. We present a Cooperative Swarm-Mesh Network (CSMN), a hybrid structure that can alternate between an implicit Silent Mode and an explicit Leader–Follower mode based on distributed Extended Kalman Filters (DEKFs) in the face of communication failures. The system takes advantage of convex polygon decomposition to optimize the coverage in the area. The use of simulation studies with NS-3 and ROS has shown that the proposed framework can retain sub-meter localization error (RMSE < 0.9 m) in GPS-denied environments and provide 92% coverage of the area, which is 35% higher than the coverage with other baseline approaches. Within the simulated conditions evaluated using Gazebo/NS-3, sensor drift and network vulnerability are effectively addressed by the CSMN framework. These simulation-based results offer a promising blueprint for autonomous disaster evaluation, pending hardware-in-the-loop and field validation. Validation is conducted across two qualitatively distinct simulated environments: dense urban rubble and a sparse open field. Performance advantages generalise beyond a single test configuration, with mean localization RMSE remaining below 0.85 m in both scenarios. Full article

The utilization of drone swarms for cooperative missions is becoming increasingly prevalent. However, establishing high-concurrency and highly reliable communication links in complex environments remains a significant challenge. Existing methods based on traditional Medium Access Control (MAC) protocols struggle to cope with high-density collisions, while conventional deep reinforcement learning (DRL) approaches often encounter convergence difficulties in non-stationary interference environments, leading to notable limitations in anti-jamming robustness and algorithmic efficiency. To tackle this problem, this paper proposes a dynamic access algorithm based on Curriculum Learning-assisted Multi-Agent Proximal Policy Optimization (CL-MAPPO). Specifically, we adopt a Centralized Training with Decentralized Execution (CTDE) architecture to enable implicit spectrum cooperation within the swarm. Notably, we design a three-stage progressive curriculum learning mechanism—basic collision avoidance, load balancing, and dynamic anti-jamming—coupled with a phased reward reshaping strategy, guiding the agents to progressively master intelligent frequency-hopping decisions in complex environments. Experimental results demonstrate that in simulated scenarios involving dynamic sweep jamming and high-load multi-drone communication, the proposed method significantly outperforms baseline models such as Carrier Sense Multiple Access (CSMA), random frequency hopping, and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in terms of normalized throughput, channel collision rate, and convergence speed. This research provides theoretical support and an algorithmic foundation for achieving highly reliable access in large-scale swarm data links under harsh environmental conditions. Full article

Multi-unmanned aerial vehicle (UAV) platforms integrate radio-frequency (RF) sensing, datalinks, and onboard embedded compute; adversarial electronic warfare (EW) degrades these subsystems through jamming and forces decentralized control policies to act on fragmented observations—a setting aligned with intelligent electronic systems and autonomous robotics in contested spectrum. Cooperative swarms then face two compounding failure modes: loss of coherent situational awareness, and reward-driven passive survival that suppresses mission completion. Memory-based multi-agent reinforcement learning (MARL) partially addresses the first but tends to reinforce the second; dense intent shaping addresses the second but becomes unreliable when observations are incomplete. We propose CIDA (Cognitive–Intent Dual-Stream Architecture), a reinforcement learning framework that decouples belief reconstruction from tactical intent at the representation level while coupling them through a unified actor–critic update. The cognitive stream encodes a 64-step observation history with a pre-normalized Transformer to reconstruct threat belief; the intent stream supplies a hierarchical potential field (reconnaissance, threat-weighted engagement, and approach incentives). A steady-state training mechanism (dynamic reward scaling and adaptive gradient clipping) stabilizes Transformer-based on-policy learning under non-stationary multi-agent dynamics. In a complex terrain scenario with SAM, AAA, and jammer assets, CIDA reaches 96.15% task success versus 12.21% (memoryless PPO) and 25.28% (MAPPO+RNN), with ablations showing nonlinear coupling and emergent tactics such as jammer bypass and weak-sector traversal. Results are robust to a four-fold sweep of the intent-shaping weight (above 90% success). Full article

Addressing the high-dimensional, strongly constrained multi-objective optimization problem of air–ground collaborative jamming scheduling in complex terrain, existing methods are often limited by incomplete modeling and low optimization efficiency in discrete feasible regions. This paper proposes a Terrain-Aware Multi-Scale Discrete Operator (TA-MSDO). A joint optimization model integrating discrete terrain characteristics and practical combat constraints is first constructed. Then, by leveraging the topological adjacency of terrain units, TA-MSDO employs a block-level crossover and a multi-scale mutation mechanism, replacing traditional continuous genetic operations to enable efficient and directional exploration of the discrete feasible region. Integrating TA-MSDO into the NSGA-III framework yields the enhanced ENSGA3 algorithm. Experimental results in a typical hilly terrain scenario demonstrate that ENSGA3 achieves a statistically significant performance improvement over the decomposition-based MOEA/D algorithm in terms of maximum achievable suppression effectiveness and hypervolume. As a comprehensive metric integrating convergence and Pareto frontier coverage, hypervolume further verifies the superior comprehensive optimization capability of the proposed algorithm. Meanwhile, compared with other classic mainstream multi-objective optimization algorithms including NSGA-II, standard NSGA-III and SPEA2, the proposed algorithm exhibits clear positive advantages in the upper bound of suppression effectiveness for elite solutions and operational stability across random initializations, with a favorable trend in Pareto frontier coverage for multi-objective collaborative optimization. This work provides an effective solution for jamming resource scheduling in complex battlefield environments. Full article

This paper addresses the cooperative jamming task allocation problem for multiple jammers against multiple communication targets in dynamic electronic warfare environments. Traditional algorithms struggle with adaptability and slow decision-making. To overcome these limitations, we propose a deep reinforcement learning (DRL) method enhanced by an improved Vogel’s approximation method (VAM) pre-training strategy, where VAM incorporates situational matrices for initial allocation. The proposed approach aims to maximize the total jamming situational value by intelligently assigning optimal target combinations to each multi-beam jammer. Specifically, the model evaluates the situational value of each target by integrating factors including the distance, target firepower, and threat levels, while adhering to system constraints of both jamming and target platforms. To meet the real-time decision-making requirements in dynamic adversarial environments, we integrate VAM with the proximal policy optimization (PPO) algorithm, leveraging human knowledge to accelerate the training process of DRL. Simulation results demonstrate that the proposed algorithm improves both the training efficiency and decision-making timeliness of the jamming allocation model, achieving cumulative reward increases of 38.45% and 13.86% over the respective baselines, while ensuring target coverage and effectively avoiding redundant or excessive jamming. Full article

Air–ground joint jamming enables three-dimensional, distributed jamming configurations, making it effective against air–ground communication networks with complex, dynamically adjustable links. Once the jamming layout is fixed, dynamic jamming power scheduling becomes essential to conserve energy and prolong jamming duration. However, existing methods suffer from poor applicability in such scenarios, primarily due to their sparse deployment and adversarial nature. To address this limitation, this paper develops a set of mathematical models and a dedicated algorithm for air–ground communication countermeasures. Specifically, we (1) randomly select communication nodes to determine the jammer operation sequence; (2) schedule the number of active jammers by sorting transmission path losses in ascending order; and (3) estimate jamming effects using electromagnetic wave propagation characteristics to adjust jamming power dynamically. This approach formally converts the original dynamic, stochastic jamming resource scheduling problem into a static, deterministic one via cognitive certainty of dynamic parameters and deterministic modeling of stochastic factors—enabling rapid adaptation to unknown, dynamic communication power strategies and resolving the coordination challenge in air–ground joint jamming. Experimental results demonstrate that the proposed Transmission Loss Ordering Algorithm (TLOA) extends the system operating duration by up to 41.6% compared to benchmark methods (e.g., genetic algorithm). Full article

In extreme scenarios, to prevent the leakage of jamming coordination information, the jammers must proactively terminate their communication functions and implement jamming resource scheduling via Non-Networked Cooperation. However, current research on this non-networked jamming approach is relatively limited. Furthermore, existing algorithms either rely on networked interactions or lack cognitive strategies for the surrounding communication countermeasure situation. For example, they fail to adapt to dynamic changes in electromagnetic noise and struggle to determine jamming effectiveness, leading to low jamming efficiency and severe energy waste in non-networked scenarios. To address this issue, this paper establishes a game process and corresponding algorithm for non-networked communication countermeasures and designs cognitive, cooperative, and scheduling strategies for individual jammers. Meanwhile, a novel performance metric called the “Overall Communication Suppression Ratio (OCSR)” is proposed. This metric quantifies the relationship between “sustained full-suppression duration” and “ operating duration of the jamming system,” overcoming the defect that traditional metrics cannot evaluate the dynamic jamming effectiveness in non-networked scenarios. Experimental results indicate that although the OCSR of the proposed Intelligent Concentric Circle Algorithm (ICCA) is significantly lower than that of the Full-Power Jamming Algorithm (FPJA), ICCA extends the operating duration of the jamming system by 4.8%. This achieves non-uniform power setting of jammers, enabling flexible and dynamic jamming in non-networked scenarios and retaining more battery capacity for jammers after overall jamming failure. Full article

In this paper, we propose a two-way hybrid satellite–terrestrial relay scheme employing Fountain codes (FCs). In the proposed model, a satellite and a ground user exchange data through a group of terrestrial relay stations, in the presence of an eavesdropper. In the first phase, the satellite and the ground user simultaneously transmit their encoded packets to the relay stations. The relay stations then apply a successive interference cancelation (SIC) technique to decode the received packets. To reduce the quality of the eavesdropping links, a cooperative jammer is employed to transmit jamming signals toward the eavesdropper during the first phase. Next, one of the relay stations which can successfully decode the encoded packets from both the satellite and the ground user is selected for data forwarding, by using a partial relay selection method. Then, this selected relay performs an XOR operation on the two encoded packets, and then broadcasts the XOR-ed packet to both the satellite and the user in the second phase. We derive exact closed-form expressions of outage probability (OP), system outage probability (SOP), intercept probability (IP), and system intercept probability (SIP), and realize simulations to validate these expressions. This paper also studies the trade-off between OP (SOP) and IP (SIP), as well as the impact of various system parameters on the performance of the proposed scheme. Full article

In recent years, with the rapid development of intelligent communication technologies, anti-jamming techniques based on deep learning have been widely adopted in unmanned aerial vehicle (UAV) systems, yielding significant improvements. Most existing studies primarily focus on intelligent anti-jamming decision-making for single UAVs. However, in UAV swarm systems, single-agent decision models often suffer from data isolation and inconsistent frequency usage decisions among nodes within the same task subnet, caused by asynchronous model updates. Although data sharing among UAVs can partially alleviate model update issues, it introduces significant communication overhead and data security challenges. To address these problems, this paper proposes a novel multi-UAV cooperative intelligent anti-jamming decision-making method, termed Federated Learning-Hierarchical Deep Q-Network (FL-HDQN). First, an adaptive model synchronization mechanism is integrated into the federated learning framework. By sharing only local model parameters instead of raw data, UAVs collaboratively train a global model for each task subnet. This approach ensures decision consistency while preserving data privacy and reducing communication costs. Second, to overcome the curse of dimensionality caused by multi-domain interference parameters, a hierarchical deep reinforcement learning model is designed. The model decouples multi-domain optimization into two levels: the first layer performs time–frequency domain decisions, and the second layer conducts power and modulation-coding domain decisions, ensuring both real-time performance and decision effectiveness. Finally, simulation results demonstrate that, compared with state-of-the-art intelligent anti-jamming models, the proposed method achieves 1% higher decision accuracy, validating its superiority and effectiveness. Full article

Autonomous vehicles (AVs) are transforming transportation by improving safety, efficiency, and intelligence through integrated sensing, computing, and communication technologies. However, their growing reliance on Vehicle-to-Everything (V2X) communication exposes them to cybersecurity vulnerabilities, particularly at the physical layer. Among these, jamming attacks represent a critical threat by disrupting wireless channels and compromising message delivery, severely impacting vehicle coordination and safety. This work investigates the robustness of New Radio (NR)-V2X-enabled vehicular systems under jamming conditions through a dual-methodology approach. First, two Cooperative Intelligent Transport System (C-ITS) scenarios standardized by 3GPP—Do Not Pass Warning (DNPW) and Intersection Movement Assist (IMA)—are implemented in the OMNeT++ simulation environment using Simu5G, Veins, and SUMO. The simulations incorporate four types of jamming strategies and evaluate their impact on key metrics such as packet loss, signal quality, inter-vehicle spacing, and collision risk. Second, a complementary laboratory experiment is conducted using AnaPico vector signal generators (a Keysight Technologies brand) and an Anritsu multi-channel spectrum receiver, replicating controlled wireless conditions to validate the degradation effects observed in the simulation. The findings reveal that jamming severely undermines communication reliability in NR-V2X systems, both in simulation and in practice. These findings highlight the urgent need for resilient NR-V2X protocols and countermeasures to ensure the integrity of cooperative autonomous systems in adversarial environments. Full article

Soft robots often suffer from insufficient load capacity due to the softness of their materials. Existing variable stiffness technologies usually introduce rigid components, resulting in decreased flexibility and complex structures of soft robots. To address these challenges, this work proposes a novel wrist-gripper composite soft end-effector based on the weaving jamming principle, which features a highly integrated design combining structure, actuation, and stiffness. This end-effector is directly woven from pneumatic artificial muscles through weaving technology, which has notable advantages such as high integration, strong performance designability, lightweight construction, and high power density, effectively reconciling the technical trade-off between compliance and load capacity. Experimental results demonstrate that the proposed end-effector exhibits excellent flexibility and multi-degree-of-freedom grasping capabilities. Its variable stiffness function enhances its ability to resist external interference by 4.77 times, and its grasping force has increased by 1.7 times, with a maximum grasping force of 102 N. Further, a grasping force model for this fiber-reinforced woven structure is established, providing a solution to the modeling challenge of highly coupled structures. A comparison between theoretical and experimental data indicates that the modeling error does not exceed 7.8 N. This work offers a new approach for the design and analysis of high-performance, highly integrated soft end-effectors, with broad application prospects in unstructured environment operations, non-cooperative target grasping, and human–robot collaboration. Full article

With the growing global demand for mineral resources, enhancing the transport capacity of heavy-haul railways (HHR) has emerged as a key area of research. As an emerging train formation technology, the virtual coupling train system (VCTS) has the potential to substantially increase the traffic density of heavy-haul trains (HHT) and thereby improve transport efficiency. However, the stable operation of virtually coupled fleets relies on train-to-train (T2T) communication, which is vulnerable to jamming attacks (JAs) within the complex operational environments of HHR. To address issues such as train decoupling and emergency braking in the VCTS that may be caused by JAs, this study proposes a stochastic game-based anti-jamming control (SGAC) strategy aimed at ensuring the stability and operational safety of the VCTS operating within HHR. The proposed approach models both JAs and defensive actions as a stochastic game and employs an $H^{\infty }$-based cross-layer control method to mitigate their adverse effects. The control performance is analyzed through frequency-domain mapping, and a quantitative evaluation is conducted using the $H^{\infty }$ norm. The simulation results demonstrate that the SGAC scheme significantly enhances the resilience of VCTS cooperative control under JAs, offering a robust solution for ensuring the stable operation of HHR. Full article

Mainlobe interference can severely degrade the performance of distributed phased-array radar systems in the presence of strong jamming or low-reflectivity targets. This paper introduces a signal–data dual-domain cooperative antijamming and localization (SDCAL) framework that integrates adaptive complete ensemble empirical mode decomposition with improved blind source separation and wavelet optimization (CEEMDAN-WOBSS) for signal-level denoising and separation. Following source separation, CFAR-based pulse compression is applied for precise range estimation, and multi-node data fusion is then used to achieve three-dimensional target localization. Under low signal-to-noise ratio (SNR) conditions, the adaptive CEEMDAN–WOBSS approach reconstructs the signal covariance matrix to preserve subspace rank, thereby accelerating convergence of the separation matrix. The subsequent pulse compression and CFAR detection steps provide reliable inter-node distance measurements for accurate fusion. The simulation results demonstrate that, compared to conventional blind-source-separation methods, the proposed framework markedly enhances interference suppression, detection probability, and localization accuracy—validating its effectiveness for robust collaborative sensing in challenging jamming scenarios. Full article