This paper proposes a novel covert transmission framework for an unmanned aerial vehicle (UAV)-reconfigurable holographic surface (RHS)-aided full-duplex (FD) integrated sensing and communication (ISAC) system, where the aerial access point (AP) simultaneously performs target sensing and downlink covert communication. We jointly design the AP’s downlink transmit signal and uplink receive beamformers, the RHS weights, the users’ uplink transmit powers, and the UAV’s trajectory, considering imperfect knowledge of the warden’s channel state information (CSI). An optimization problem is formulated to maximize the minimum covert transmission rate (CTR) among all downlink covert users (DCUs), subject to constraints on required sensing and uplink transmission capabilities, covertness, and total power budget. To tackle the intractable non-convex problem, we leverage the Bernstein-type inequality, majorization-minimization (MM), and successive convex approximation (SCA), and propose a secure optimization framework that efficiently updates all variables using convex optimization techniques. To further understand the proposed algorithm, its convergence behavior and computational complexity are discussed. Simulation results demonstrate that integrating RHS and UAV techniques into the optimization design enhances the covert transmission performance of FD-ISAC systems while ensuring a certain level of sensing capability.

This paper proposes optimizing a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system in the vehicular networks to sense a potential eavesdropping target and transmit the confidential information to the legitimate cellular vehicular users (CVUs). An optimization problem is formulated by maximizing the sum system secrecy rate of all vehicle-to-infrastructure (V2I) links subject to waveform similarity and radar echo signal-to-interference-plus-noise ratio (SINR) constraints. To address the challenging non-convex problem, we first cast the formulated problem into an equivalent form relying on the mean-square error (MSE) technique given the overall resource budget, and then develop a sequential optimization procedure to update all optimization variables sequentially. Specifically, to solve the dual-function waveform optimization subproblem, we leverage a dual ascent approach (DAA) and the Limited-memory Broyden Fletcher Goldfarb and Shanno (LBFGS) technique. Simulation results confirm the effectiveness of the developed design method indicating a secure communication with improved performance against the state-of-the-art ISAC schemes.

Hybrid satellite-unmanned aerial vehicle (UAV)-terrestrial networks (SUTNs) can provide maritime users with ubiquitous communication services. However, eavesdropping poses a significant challenge to the secure communications of SUTNs due to their wide-area coverage. In this paper, we propose a novel secure scheme for maritime communications, where a terrestrial-UAV integrated network coexists with marine satellite (MS) systems in the presence of an eavesdropper (Eve). Considering imperfect channel state information (CSI) for both the MS and Eve, we focus on the collaborative design of beamforming for the terrestrial base station (TBS), UAV, and MS, as well as the UAV’s trajectory. A robust optimization problem is formulated to maximize the worst-case secrecy rate, subject to constraints on worst-case communication quality for each user, UAV locations, and TBS backhaul throughput. To tackle this intractable non-convex problem, we leverage the S-procedure, general sign-definiteness, and successive convex approximation (SCA) to propose a security solution that efficiently optimizes all variables using convex optimization techniques. Numerical results validate the effectiveness of the proposed solution, illustrating the impact of CSI errors and the secure performance enhancements achieved through joint trajectory and beamforming optimization.

This paper investigates joint transmit beamforming and trajectory optimization techniques for an unmanned aerial vehicle (UAV)-enabled covert full-duplex (FD) integrated sensing and communication (ISAC) systems with hardware impairments (HWIs), where the aerial access point (AP) transmits and receives sensing signals while the integrated communication operates in either downlink or uplink. We jointly optimize the downlink transmit signal and the uplink receive beamformers at the AP, the transmit power at the uplink users and the trajectory of the UAV. An optimization problem is formulated for maximizing the minimum covert transmission rate (CTR) among all covert users (CUs) subject to the constraints of the required sensing and uplink transmission capabilities, system covertness, total power budget. To tackle the intractable non-convex problem, we leverage majorization-minimization (MM) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Numerical results demonstrate the effectiveness of the proposed method in balancing the trade-off between covert communication and sensing performance, highlighting the UAV’s potential in adaptive ISAC deployment.

In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom) enabled integrated sensing, communication and computation (ISCC) systems. We consider the joint design of transmit beamforming at the BS and the reflecting coefficients at the RIS as well as the computation resource allocation of each user. The optimization problem for the max computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the alternative optimization (OA) and the alternating direction method of multipliers (ADMM) algorithm is developed. Furthermore, a more computationally efficient approach is introduced, which utilizes transmit beamforming based on an accelerated primal gradient (APG) method. Furthermore, the approximation principle is proposed to transform non-convex constraints in the optimization of the reflection coefficients at RISs. Simulation results show that introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

Vehicle-to-everything (V2X) applications are usually powered by vehicular batteries and thus are power limited in general. Reconfigurable intelligent surfaces (RISs) are capable of improving the spectral efficiency and conserving energy of the wireless communications, due to the planar array architecture of which is superior beamforming gain and energy-efficient. In this paper, we study a novel design scheme where a double-RIS-enabled integrated sensing and communication (ISAC) system in vehicular network performs both a single target sensing and multi-user communications synchronously. Specifically, two transmit power budget minimization problems are formulated based on Cramér-Rao bound (CRB)-based framework under the known target location model, and radar signal-to-noise ratio (SNR)-related framework under the uncertain target location model, respectively. For the former, we propose an efficient solver based on alternative direction method of multipliers (ADMM) technique to obtain high-quality solutions for transmit beamforming and phase shifts. For the latter, an efficient algorithm based on penalty-dual-decomposition (PDD) and second order cone programming (SOCP) approaches is proposed. Simulation results demonstrate the effectiveness of two proposed algorithms and also show the superiority of our developed schemes over state-of-the-art benchmark ISAC schemes.

Estimating human pose and shape without markers is a challenging problem. This study proposes a multiple‐view markerless human motion capture framework. Firstly, a multi‐view camera system is built for capturing real‐time images of moving humans on multiple views. Secondly, by employing the OpenPose method, the authors calculate robust 3D key points from 2D key points of the human body, which are estimated from the multi‐view images. And dense 3D point cloud is reconstructed from images. Thirdly, they propose a novel SMPL‐based method to represent human motion by fitting the SMPL model to 3D key points and 3D point clouds. In order to achieve a more accurate human pose, a penalty term is utilised to solve the problem of error accumulation in the process of human motion capture. In addition, they present a dense mesh template‐based SMPL that can be deformed to point cloud to recover a real human body shape. Finally, they map multi‐view colour images onto the human mesh model to acquire rendered mesh. The experimental results show that the proposed method improves the accuracy of human pose and realises the 3D human body model more realistic.