To address the decline in positioning accuracy caused by long-term GNSS observation outages under tightly coupled (TC) in urban canyons, this paper proposes a pseudorange compensation mechanism based on a bidirectional long short-term memory network with multi-scale convolutional attention (BiLSTM-MSCA). Under frequent occlusion of satellite signals, the proposed network is used to learn the pseudorange incremental relationship between INS information and GNSS signals, and then compensate for the GNSS pseudorange observations. The proposed BiLSTM-MSCA utilizes the bidirectional information of input INS and GNSS signals in the time domain and enhances the extraction of key information to improve the prediction accuracy of the network. Experiments based on the measured data of urban canyons show that the horizontal positioning accuracy of the proposed method is improved by 20 % compared with the existing neural network assisted method under the condition of 100s GNSS observation loss.

Recognizing the coverage limitations of traditional reconfigurable intelligent surface (RIS), the simultaneously transmitting and reflecting RIS (STAR-RIS) with the ability to provide coverage on both sides of the surface, has been regarded as a promising technology especially for the integrated indooroutdoor communication environment. In this paper, we investigate a STAR-RIS assisted unmanned aerial vehicle (UAV)-enabled integrated sensing and communication (ISAC) system in the presence of imperfect channel state information (CSI). Specifically, the UAV serves as an aerial base station to provide communication services to outdoor users, as well as positioning for indoor users. To maximize the communication sum-rate while satisfying the sensing beampattern gain requirement, the UAV’s trajectory, beamforming, and the STAR-RIS transmission/reflection coefficients are jointly optimized. Meanwhile, the UAV’s flight safety, the maximum flight duration, and the minimum communication rate for each user are also considered. An online decision-making framework that utilizes deep reinforcement learning (DRL) is proposed to address the sum-rate maximization problem with imperfect CSI. Furthermore, in order to decouple the continuous optimization variables and improve the applicability of the system, we introduce a twin-twin-delayed deep deterministic policy gradient algorithm based on self-attention mechanism (SA-TTD3), which utilizes dual agents to decouple and optimize variables, and incorporates attention mechanisms to improve applicability. The numerical results indicate that the proposed SA-TTD3 algorithm significantly improves the performance of the ISAC system compared with the baseline schemes.

This paper explores a near-field integrated sensing and communication (ISAC) system enabled by a beyond diagonal reconfigurable intelligent surface (BD-RIS), where the target and users are located at the reflective and refractive space, respectively. In contrast to most existing RIS-aided ISAC systems that employ a single-connected architecture, we investigate group-and fully-connected BD-RIS architectures. Moreover, depending on the availability of prior information about the extended target, we propose a parametric scattering model (PSM) and an unstructured channel model (UCM). For PSM and UCM, we derive the Cramér-Rao bound (CRB) of target positions and the response matrix, respectively. Both CRB minimization problems are investigated: 1) For CRB minimization in PSM, we develop the alternative optimization (AO) algorithm that leverages semidefinite relaxation (SDR) and penalty-based manifold optimization (PBMO). 2) For CRB minimization in UCM, we prove that the optimal beamformers reside on a complex matrix sphere manifold and utilize the PBMO to jointly optimize. Finally, the numerical results show that: 1) The developed schemes consistently outperform the benchmark counterparts in sensing performance, irrespective of whether prior information is available; 2) The additional prior information can effectively improve the sensing accuracy of the extended target.

Internet of Things (IoT) devices often deliver sensitive sensing and control data over wireless links that are inherently broadcast, making passive over-the-air eavesdropping a persistent threat even for simple point-to-point transmissions. Many physical-layer security (PLS) techniques, however, rely on accurate channel knowledge, additional spatial degrees of freedom, or strong secrecy assumptions that are often hard to guarantee in practical IoT links. In this paper, we propose an anti-eavesdropping secure transmission scheme by deliberately embedding controllable artificial interference into a conventional BPSK waveform on the same carrier, thereby forming an interference-embedded waveform transmitted over an AWGN channel. To exploit the common architectural asymmetry in IoT systems, the legitimate receiver located at an IoT gateway/edge node employs an Anti-Eavesdropping Waveform Demodulator (AEWD), a ResNet–self-attention–BiLSTM neural demodulator trained end-to-end to recover bits directly from raw I/Q samples without explicit interference parameter estimation. Under identical channel conditions, conventional model-based receivers that perform deterministic interference suppression followed by demodulation incur substantial BER degradation and frequently exhibit interference-limited error floors. Extensive simulations across a wide range of SNR and SIR demonstrate that AEWD consistently outperforms classical baselines under both single-tone and multi-tone nonstationary interference. The results suggest that waveform-level interference embedding combined with a dedicated neural demodulator can create a practical receiver performance gap to strengthen confidentiality for IoT communications.

To address the practical challenges of satellite-assisted mobile communication for Internet-of-Things (IoT) devices—such as the needs for low-profile antennas, wide-angle beam steering under strict size and power constraints, and the limited tuning flexibility of conventional reconfigurable structures—this work proposes a compact dual-polarized antenna with digitally controlled pattern steering. The method combines mirror-image theory with transmission-optics-based phase regulation, enabling beam steering through a reconfigurable digital meta-surface that replaces the conventional ground plane. A planar dual-polarized dipole is positioned above the meta-surface, and beam steering is achieved by digitally switching the PIN-diode states to alter the discrete reflection-phase distribution. The prototype, fabricated and characterized at 12–14 GHz, demonstrates wide-angle beam steering with stable gain, low cross-polarization, and FPGA-based real-time digital control. The proposed approach provides a compact and energy-efficient solution for next-generation satellite-assisted IoT terminals requiring flexible and robust beam steering.

Wireless charging is a cornerstone technology for next-generation mobile and ubiquitous computing. However, its practical deployment has long been constrained by short range, poor flexibility, and lack of support for dynamic multi-device scenarios. In this paper, we propose ChargeX—a system that enables long-range and mobility-resilient wireless charging for multiple small devices. ChargeX pioneers the integration of metasurface-assisted magnetic beamforming, a high-frequency compact transceiver design, and a real-time closed-loop feedback-control mechanism. It further advances the field by introducing a joint optimization framework for dynamically allocating energy across mobile receivers with heterogeneous priorities and spatial-temporal demands. Experimental results demonstrate that it achieves meter-level charging distance, real-time response to device movement, and efficient coordination among multiple receivers, significantly outperforming state-of-the-art prototypes.

UAV-assisted distributed IoT data collection plays a vital role in scenarios ranging from post-disaster response to large-scale temporary events. This paper investigates dynamic resource allocation for UAV-assisted IoT uplink transmission under partial channel state information (CSI). Considering the impracticality of acquiring full CSI due to pilot overhead and limited device computing capabilities, the problem is modeled as a Partially Observable Markov Decision Process (POMDP). A dynamic scheduling strategy is proposed, jointly optimizing node selection, beamforming weights, and UAV trajectory. By leveraging belief updates to integrate noisy channel observations with historical information, the proposed approach enhances decision-making under uncertainty. Furthermore, we introduce a novel channel-aware belief-space rollout (CABR) algorithm, which combines reliability-driven action candidate generation, a weighted multi-factor reward function, and adaptive planning depth based on task process to efficiently allocate resources under partial CSI. Simulation results demonstrate that the proposed method significantly improves throughput and reduces latency compared to the state of the art.

This paper proposes an integrated communication and jamming system design to address the requirements of secure information transmission and interference in modern electronic warfare environments. We develop a signal transmission system capable of generating band-limited white noise-like spectral characteristics within target frequency bands, and introduce an intelligent superposition algorithm that achieves high-precision disguise of band-limited white noise through optimized configuration of carrier signal frequencies and amplitudes. The system employs Non-Orthogonal Multiple Access (NOMA) technology for signal demodulation, transmitting multiple signals carrying identical information over the same time-frequency resource, and enhances reception reliability through successive interference cancellation. We analyze the impact of key parameters—including superimposed signal quantity, adjacent signal power differential, signal-to-noise ratio, and transmission time interval—on spectral similarity and bit error rate performance. Simulation results demonstrate that the proposed intelligent superposition algorithm achieves significantly higher spectral similarity compared to random allocation methods, while ensuring communication reliability with appropriate parameter configurations. This scheme is particularly applicable to military communication scenarios requiring simultaneous covert communication and jamming capabilities, offering substantial practical value for tactical operations.

The cross-polarization backscatter positioning system based on the information-energy decoupling mechanism can achieve high gain and obstacle avoidance advantages while reducing the number of positioning antennas. A novel dual-band quad-polarized antenna design with in-band radar cross section (RCS) reduction using polarization conversion metasurface (PCM) for IoT anti-multipath indoor positioning is first proposed in this article. At first, a dual-band reflective miniaturization PCM unit is designed to analyze the relationship between the polarization conversion band and in-phase reflection band under u- and v- polarized incident waves. This provides theoretical support for PCM to simultaneously achieve radiation enhancement and in-band RCS reduction. Then, a dual-band shaping dipole antenna is designed as a radiator. Finally, a dual-band Wilkinson power divider and Butler matrix are designed to feed the$2\times 2$dipole array to achieve quad-polarization. In the overall design of the antenna, according to the principle of in-phase reflection in the dual-band, every$4\times 4$subarray of PCM is used as a reflector for every dipole antenna in the v direction to realize unidirectional radiation and low-profile design. The quad-polarization of X, Y, left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP) is achieved within the bandwidths of 0.85–0.93 GHz and 2.38–2.5 GHz. Based on the principle of reflection phase cancellation in the dual-band, an$8\times 8$arrangement of PCM units with their mirror units is designed to achieve in-band monostatic RCS reduction in the dual-band range of 0.745–1.02 GHz and 2.1–2.67 GHz. Therefore, this antenna is highly suitable for the application of item-level high-precision cross-polarization positioning systems in multiconductor environments with indoor deployment of multiple IoT nodes.

Due to the advantages of flexible deployment and strong environmental adaptability of an unmanned aerial vehicle (UAV), UAVs serve as aerial base stations (BSs) to meet Quality of Services (QoSs) of ground users in internet of things (IoT) networks. NOMA (Non-Orthogonal Multiple Access) is a potential technique in wireless communications area, which can significantly improve sum spectrum efficiency (SE) of systems. To avoid the limitation of perfect CSI, opportunistic beamforming (OBF) is proposed, where a set of randomly generated weights is used to preprocess transmitted signals. Due to multiuser diversity gain introduced by OBF, OBF-NOMA systems can achieve approximate sum SE to conventional NOMA systems. Additionally, reconfigurable intelligent surfaces (RISs) are involved to overcome obstruction and obtain further improvements of SE. Therefore, this paper proposes a RIS-aid OBF-NOMA system in UAV-IoT networks, where random weights and opportunistic phase matrix are respectively applied in a UAV and RIS. Statistical characteristics of equivalent channels are derived in Nakagami-m (m≥1) fading channels. Theoretical asymptotic analyses of SE and bit error rate (BER) are then presented. Furthermore, a non-convex optimization problem is formulated to maximize SE. To obtain the optimal solution, we divide the problem into two sub-optimization problems and apply a joint iterative algorithm. Numerical results show that the proposed method achieves a satisfactory SE without complex channel estimation and perfect CSI.

The detection of small floating targets is a challenging problem for maritime surveillance radar. To achieve effective detection within complex sea clutter background, an innovative graph feature detector is proposed in this letter. First, the received radar sequences are converted into graphs to capture the correlation of signals. Then, three graph features weight peak height (WPH), graph complexity (GC), and graph entropy (GE) of weighted difference visibility graph (WDVG) are proposed. The topological properties of the WDVGs constructed from the phase domain of radar echoes is analyzed, which provides insights into the underlying dynamics structures of the observed phenomena. In the detection part, an improved false alarm rate controllable (FAC) concave detector is designed, which is based on the concave hull-learning algorithm. Experiments results based on the real measured IPIX radar datasets confirm that the proposed method has a better performance compared with the existing feature-based methods, especially under shorter observation time (0.128 s).

Radio Frequency Interference (RFI) would lead to degradation of image quality for synthetic aperture radar (SAR) systems, resulting in a waste of observation resources. RFI source localization provides crucial prior information for RFI mitigation and spectrum management, and traditional RFI localization methods for multichannel SAR system suffer from localization ambiguity. This paper derives the RFI model for dual-channel SAR and investigates the mechanisms underlying localization ambiguity. A localization framework is proposed based on the varying characteristics of RFI with platform motion and the slant range difference model between dual-channels. This approach achieves localization by implementing mutual constraints among alternative optimization models. Moreover, an RFI filtering scheme is developed to facilitate the mutual cancellation of RFI between the two channels. The performance of the proposed method is validated using simulated RFI and real-measured RFI scenarios of Chinese Lutan-1 mission. The results indicate that the proposed method effectively mitigates the localization ambiguity, demonstrating high-precision localization performance. Moreover, it can reduce echo amplitude distortion and preserve degrees of freedom while removing RFI effectively.

Vehicle positioning and tracking play an essential role in intelligent transportation systems, especially under the growing demands of autonomous driving, traffic management, and path planning. However, most existing works adopt classical two-step methods and suffer from suboptimal performance due to intermediate estimation errors. In this work, we propose a direct vehicle tracking method based on adaptive PARAllel FACtor (PARAFAC) decomposition, referred to as DT-AP, which differs from conventional tracking frameworks by operating directly on the received signal. We first reveal that the received signals can be naturally modeled as a low-rank dynamic streaming tensor, representing the multi-dimensional and time-evolving characteristics of vehicle motion in distributed sensing systems supported by 5G ultra-dense networks. By adaptively decomposing the streaming tensor, DT-AP enables online trajectory estimation while eliminating intermediate processing and thereby reducing information loss. Numerical simulations are conducted to validate the effectiveness of the proposed method under dynamic multipath environments. Simulating results demonstrate that DT-AP outperforms traditional tracking approaches in both accuracy and computational complexity, while maintaining robustness under multipath conditions. These features indicate its potential for real-time and reliable applications in intelligent transportation systems.

Unmanned aerial vehicle ad hoc networks (UANET) possess several advantages, such as distributed operation, self-organisation and long-range communication, leading to an exten-sive application in the field of Internet of Things (IoT). However, in practical UANET assisted IoT network scenarios, high degree of dynamism in UAV network topology, coupled with the complex and harsh conditions, adversely affects communication quality. Cluster networking is a well-established and highly effective technique for both organizing and managing UANET. The primary goal of cluster networking is to enhance the networks connectivity and stability by dividing nodes into clusters. This paper presents a novel weighted cluster networking mechanism, which aims to improve the performance of networks. The proposed mechanism involves two primary stages. In the initial networking stage, lowest identification number algorithm is employed to group the nodes. Subsequently, a weighted networking stage utilizes four metrics to enhance the clustering process. The optimal number of clusters is then determined by solving an optimization problem. Simulation results show that our proposed mechanism improves throughput, reduces delays and has better stability and scalability than traditional algorithms.

With the development of internet of things (IoT) technology and its wide application in urban traffic, the next-generation vehicle-to-everything (V2X) communication network should support high-capacity, ultra-reliable, and low-latency massive information exchange to provide unprecedentedly diverse user experiences. The development of the sixth-generation (6G) mobile communication technology will pave the way for realizing this vision. Reconfigurable intelligent surfaces (RISs), a critical 6G technology, is expected to make a big difference in V2X communications when used in conjunction with unmanned aerial vehicles (UAVs), allowing for extremely increased communication capacity and reduced latency. We propose a UAV-enhanced RIS-assisted V2X communication architecture (UR-V2X) suitable for urban three-dimensional (3D) IoT traffic and design an adapted MAC protocol UR-V2X-MAC to accomplish communication resource allocation and scheduling. The UAVs are used as access points and resource allocation centers, while the RISs are used as passive relays to assist V2X communication in proposed architecture. To improve the performance of UR-V2X-MAC, we use a distributed optimization algorithm in the message report phase of the protocol to maximize the system capacity by allocating the transmit power and alternately optimizing the RIS phase shift matrix. We analyze the delay and system capacity characteristics under different parameter settings through theoretical derivation and protocol performance simulation. Analysis and simulation results are presented to demonstrate that UR-V2X-MAC achieves a reduction in communication delay and a significant increase in system capacity through detailed design and alternate optimization compared to the existing V2X MAC protocol and no-RIS case.

Due to high flexible deployment and enhanced transmission capabilities, unmanned aerial vehicle (UAV) has attracted significant attention in recent years. UAV can serve as base station to provide communication coverage for emerging internet of things (IoT) in hot-spot areas. Stable topology plays an important role in improving connection and efficiency of UAV-IoT networks. However, when UAV-IoT nodes fail, network topology is destoryed and reconstruction becomes a formidable challenge, particularly in extremely harsh scenarios. Traditional topology reconstruction schemes predominantly rely on node movement to restore network connectivity, which neglect the performances of UAV-IoT networks. To remain stability and promote performances simultaneously, this paper proposes a distributed resource scheduling topology reconstruction (DRSTR) scheme, where both connectivity and throuphput of UAV nodes are jointly considered. Then, an optimization problem combining topology reconstruction and resource allocation is presented under the constraints of UAV-IoT nodes’ quality of service (QoS) requirements. Since the difficulty in solving the problem, the original problem is divided into three sub-optimal issues and an iterative approach is introduced to approximate the global optimal solution. Numerical results show that the proposed shceme achieves higher performances in UAV-IoT networks, compared to the traditional topology reconstruction shcemes.

Industrial Internet of Things (IIoT), regarded as an important technology for Industry 4.0, has the capability to connect massive IoT devices anywhere and at anytime in manufacturing industry. Enabling such a huge network requires message delivering among sensors, actuators, controllers, and the remote control to be seamless and reliable. However, IIoT wireless environment typically faces challenges such as blockage caused by IoT obstacles. To tackle the above issue, the unmanned aerial vehicle (UAV) and the reconfigurable intelligent surface (RIS) are exploited in this paper, which can provide favorable air-to-ground links and further rebuild the wireless channels. Moreover, the device-to-device (D2D) communication technique is introduced to enable direct information exchange between IoT devices. Specifically, we consider both the communication between the UAV and the cellular users (e.g., fixed IoT infrastructures) as well as the communication between D2D users (e.g., mobile IoT devices). Instead of only considering throughput, we focus on energy efficiency optimization for D2D users while guaranteeing the quality of service for cellular users, since energy-efficient transmission or green communication is important for IIoT scenarios. The transmit power, channel allocation parameters, and RIS’s reflection coefficients are jointly optimized to maximize energy efficiency for D2D users. To solve the formulated optimization problem, both centralized and distributed optimization algorithms based on deep neural networks are provided. Simulation results show that the introduction of RIS can significantly improve system performance. Moreover, the proposed algorithms can approximate the optimal solutions without the need of exhaustive search.

Range ambiguity is a common issue for spaceborne synthetic aperture radar (SAR) systems, leading to image quality degradation and subsequent interpretation accuracy. Existing range ambiguity suppression methods mainly rely on raw echo domain processing with specific requirements on prior knowledge. However, most users can only obtain single-look-complex(SLC) products instead of raw echo products. Raw echo obtained from SLC through an inverse focusing process will waste additional time and space resources. This paper proposes a novel scheme to detect and suppress range ambiguity in SLC products to deal with this deficiency. The proposed method designs a detector to extract and suppress the focused range ambiguity based on the characteristic difference between the desired signal and range ambiguity in the fractional transform domain of the SLC image. Experimental results on real measured L-band spaceborne interferometry SAR system verify the detection performance of the proposed method, which is beneficial for subsequent land monitoring applications.

Satellite imagery plays a critical role in target detection. However, the quality and usability of optical remote sensing images can be severely compromised by atmospheric conditions, particularly haze, which significantly reduces the recognition accuracy of target detection algorithms such as ships. On the other hand, paired training data, i.e., the remote sensing data with or without fog at the same place, are difficult to obtain in real-world scenarios, leading to the failure of many existing dehazing methods. To deal with these issues, this article proposes a Transformer-Guide CycleGAN framework generative adversarial networks (Dehaze-TGGAN) incorporating an extra attention mechanism from the frequency domain. First, an SSA mechanism is proposed by using a 2-D fast Fourier transform (2D FFT) in the spatial domain, which enables the model to understand the relationships within the three-channel frequency domain information and to recover the spectral features of the hazy image through the spectrum encoder block. Then, a pre-training approach using semi-transparent masks (STM), which can effectively simulate hazy conditions by adjusting the transparency of masks, is presented as a key strategy to accelerate the convergence rate. Finally, the applicability of the transformer architecture is extended by incorporating total variation loss (TV Loss). The results of simulated and measured optical remote sensing data show that the recognition accuracy and the efficiency of the proposed algorithm are greatly improved.

Heterogeneous cellular structure is a potential scheme in the sixth generation (6G) network, where macro and micro base stations (BSs) are collaborated to improve quality of service (QoS) for users. For the conventional heterogeneous cellular networks, data of users is transmitted at one frequency band and perfect channel state information (CSI) is required to beamforming. However, adjacent frequency interference and imperfect CSI decrease the spectrum efficiency (SE) of cellular network and QoS of users. In order to deal with performance loss of SE and QoS, this paper proposes a cognitive radio (CR)-based heterogeneous cellular networks, where opportunistic scheme is applied to improve QoS of users with imperfect CSI. Then, an optimization problem is established, considering both resource allocation and user selection under the constraints of users' QoS requirements. For the convenience of solving, we divide the optimization problem into two suboptimal issues and use an iterative method to approximate the global optimal solution. Numerical results reveal that the proposed scheme can achieve higher SE than the conventional scheme in heterogeneous cellular networks.

Abstract Polarisation‐sensitive array (PSA) has earned extensive attention in satellite communication owing to excellent anti‐interference performance. Nevertheless, the configuration of traditional PSA where each polarised antenna requires multiple radio frequency (RF) front‐ends makes it much more costly in terms of hardware and software for system design. In this paper, resorting to RF switches, a novel reconfigurable PSA optimisation technique is developed that utilises fewer RF front‐ends, which can considerably decrease computational expenditure and achieve high anti‐jamming performance. An RF switch switching (RFSS) scheme is devised to guide the implementation of PSA reconfiguration. To accurately demonstrate the effect of the array configuration on interference rejection performance, the polarisation‐spatial subspace correlation coefficient (PSC) is presented. Then the relationship between the optimal signal to interference plus noise ratio (SINR) and the PSC based on adaptive processing is formulated. Aiming to acquire the optimal reconstructed PSA outputting the maximum SINR, the mathematical model of the PSA reconfiguration problem is established. Subsequently, two optimisation methods are provided to address the problem efficiently, thereby gaining the optimal configuration of the reconstructed PSA and implementing the reconfiguration by RF switches. Numerical and experimental simulations verify the correctness and reliability of the developed scheme and approaches.

Opportunistic beamforming (OBF) is a promising multiple-input–multiple-output (MIMO) downlink precoding technique, where multiuser diversity gain is achieved to deal with the performance loss caused by low-complexity channel estimation. Due to high-performance and low-complexity channel estimation, OBF is appropriate for Internet of Things (IoT) networks. However, the conventional OBF cannot achieve satisfactory performance in post-disaster communication, and the low signal-to-noise ratio (SNR) makes it hard to establish communication between the base station (BS) and users. Therefore, we propose an unmanned aerial vehicle (UAV)-assisted OBF IoT network (UON) to satisfy the Quality-of-Service (QoS) requirements of users. Then, an optimization problem is formulated to maximize the weighted energy and spectrum efficiencies, while the optimization problem is nonconvex and hard to solve. To obtain the solution, we divide the optimization problem into three suboptimal issues, and then a joint iterative algorithm is applied. According to numerical results, the proposed scheme achieves higher system performances compared with the conventional OBF scheme in both Rayleigh and Rician channels. Moreover, we discuss the objective functions with different weights of spectrum efficiency (SE) and energy efficiency (EE) to adapt the QoS requirements of different scenarios. It is indicated that the proposed scheme can achieve high performance with low computational complexity, regardless of the weighted energy and SE.

In this paper, we investigate the covert communications in uplink nonorthogonal multiple access (NOMA) systems with the random power allocation and multi-carrier modulation scheme to confront a two-phase detector. In the NOMA system, a covert user (CU) and a reliable user (RU) transmit messages to Bob, in the presence of a warden (Willie) who tries to detect CU's transmission behavior. A two-phase detector, i.e., energy detection and similarity detection phases, is designed to improve the detection performance. In addition, we propose a random power allocation and multi-carrier modulation scheme to cover CU's transmissions. For the proposed scheme, the expected minimum detection error probability (EMDEP) and connection outage probabilities (COPs) at RU and CU are derived in closed-form expressions. The maximum expected covert rate (ECR) is analyzed under covertness and reliability constraints. Numerical results show that the proposed two-phase detector has a lower EMDEP, and the multi-carrier modulation scheme improves the covertness performance.

Stability and security are critical to modern wireless communication systems, which have attracted more and more attention in recent years. In this paper, physical layer security (PLS) between the transmitter and the legitimate user (LU) is greatly enhanced by inventing a robust directional modulation (RDM) scheme based on a relaxation phase constraint. Unlike the conventional perfect channel state information (CSI) assumption, the estimation error of LUs' azimuths is assumed, which corresponds to a more practical scenario. Moreover, the influence of the imperfect CSI on the design of artificial noise (AN) vector is investigated. Accordingly, a specific strategy to introduce AN at the transmitter is proposed with imperfect CSI. Numerical simulations suggested that the RDM scheme based on the relaxation phase constraint possesses great potential for the enhancement of security of wireless communications.

Maritime target detection is one of the most complicated problems in the radar signal processing field. Since traditional constant false alarm rate detection methods rely on the clutter distribution model, the mismatch of the sea clutter model leads to a decrease in the target detection performance. In this paper, the amplitude statistical entropy (ASE) of sea clutter sequence is extracted as a feature to describe the degree of aggregation of the sea clutter amplitude statistical histogram. Then a novel target detection algorithm based on ASE is proposed, which is not affected by the degree of the model matching between sea clutter datasets and statistical model. Finally, the experiment result based on the Canadian IPIX radar datasets confirms the effectiveness of this method.

Abstract A sparse reconstruction algorithm for the localisation of mixed near‐field and far‐field sources (MFNS) based on four‐order statistics is proposed in this study. First, utilising the structural characteristics of a uniform symmetric linear array, a fourth‐order cumulant (FOC) matrix is constructed, which decouples the angular information from the range parameters. Based on the sparse representation framework, a weighted l 1 ‐norm minimisation algorithm is developed to obtain the direction of arrivals (DOAs) of the MFNS. However, the existing selection strategy of the tuning factor is not adaptive to different observation scenarios. So a closed‐form expression of the tuning factor based on the FOC estimation error is presented. Then, another FOC matrix is constructed, which includes both the DOA and range information of the MFNS. With the DOA estimates, the two‐dimensional spatial dictionary can be reduced into a one‐dimensional dictionary, which only depends on the range parameters. Using the similar sparse reconstruction method, the range estimates of the MFNS can be obtained, and the types of the sources can be distinguished according to their range parameters. According to numerical simulations, the estimation performance of the proposed algorithm approaches the CRB in the high signal‐to‐noise ratio region, which successfully circumvents the saturation problem due to the fixed tuning factor.

Wide-band interference (WBI) may severely affect the imaging quality of synthetic aperture radar (SAR) systems. Since it highly overlaps with useful signals in the 1-dimensional (1-D) time or frequency domain, the existing WBI mitigation methods usually transform 1-D echoes into a 2-D transform domain. However, they usually suffer from a model mismatch, which results in the loss of the useful signal. To tackle this problem, a novel algorithm combining time-frequency-pulse (TFP) joint characteristics and robust principal component analysis (RPCA) is proposed for WBI mitigation. The TFP joint feature of SAR echo is introduced for interference mitigation for the first time. We first transform the SAR echoes into the time-frequency domain, and construct a new TFP matrix by reshaping the STFT matrices between adjacent pulses. In terms of the WBI-occupied SAR echoes, the short-time Fourier transformation (STFT) in adjacent pulses can be modeled as a combination of a low-rank part (i.e. useful SAR echoes) and a sparse counterpart (i.e. WBIs), which well fits the assumption of RPCA. Then, the TFP matrix is decomposed into the useful signal TFP matrix and the WBI TFP part by taking full advantage of the low-rank and sparse properties. Finally, the WBIs can be reconstructed and subtracted from the echoes to realize interference mitigation. Experimental results on both simulated and measured datasets show that the proposed algorithm not only suppresses WBIs effectively but also preserves useful information as much as possible.

Radio frequency interference (RFI) is a significant threat to accurate microwave remote sensing. The RFI signals manifest themselves in unpredictable locations and patterns in the image, which will cause measurement distortion, image degradation, or even lead to wrong retrievals of the geophysical parameters. Accurate detection of RFI artifacts is a prerequisite step to preserve the overall quality of remote sensing quality. In this paper, a semantic cognitive enhancement network for RFI signature detection is proposed. It employs an encoder-decoder architecture, which incorporates the atrous spatial pyramid pooling, Depthwise convolution, and self-attentional mechanism. Rather than detecting the existence of RFI artifacts for an entire image, the proposed scheme can realize RFI recognition in a pixel-wise manner without setting predefined thresholds. Extensive experimental results on diverse scenarios in Sentinel-1 images with various RFI types are provided, which demonstrates robust detection performance for both strong and weak interference without requiring a large number of training samples.

Radio frequency interference (RFI) is a critical issue for accurate remote sensing by synthetic aperture radar (SAR). Existing literature mainly detects and mitigates RFI in the raw data domain, which is generally not accessible to the end-user. In this article, a novel RFI extraction and mitigation scheme in the image domain is proposed using multitemporal analysis of SAR images. By exploiting the coupling correlation and complementary information among the time-series images, the background landscape could be modeled as relatively stationary with the low-rank property. Meanwhile, the radiometric artifacts corresponding to RFI could be well extracted and characterized by the sparse components. Extraction and mitigation of RFI signatures could be achieved simultaneously via a joint iterative optimization process. Experimental results on typical real-measured Sentinel-1 datasets acquired in different regional areas with various RFI types demonstrate the validity of the proposed method.

Remote sensing image dehazing is of great scientific interest and application value in both military and civil fields. In this article, we propose an enhanced attention-guide generative adversarial network (GAN) network, Dehaze-AGGAN, to solve the remote sensing images dehazing problem, which does not require paired training data. Since haze images have a great influence on remote sensing object detection, the dehazing of remote sensing images has become significantly important. Typical image dehazing methods require a hazy input image and its ground truth in a paired manner, while paired training data are usually not available in the field of remote sensing. To solve this problem, we propose the Dehaze-AGGAN network and train it by feeding unpaired clean and hazy images into the model. We present a novel total variation loss combined with the cycle consistency loss to eliminate wave noise and improve the target edge quality in the test dataset. Moreover, we present a new dehazing dataset called remote sensing dehazing dataset (RSD), which contains 7000 simulate and real hazy images including 3500 warship images and 3500 civilian ship images, and evaluate our method in the dataset. We conduct experiments on RSD. Extensive experiments demonstrate that the proposed Dehaze-AGGAN is effective and has strong robustness and adaptability in different settings.

In this paper, a novel efficient algorithm is developed for direction of arrival (DOA) estimation of coherent signals under the direction-dependent mutual coupling (DDMC) based on weighted subspace fitting. DOAs are determined by applying the least square fitting between signal space and the modified array manifold at first. Subsequently, we put forward an approach to calculate the DDMC matrices and the complex fading coefficients by utilizing the estimated DOAs. Without any iterations, the proposed algorithm can identify the angular information of the coherent signals in a single step in the presence of DDMC. Numerical simulation results show the effectiveness of the proposed algorithm.

On the ground with complex electromagnetic environment, protecting the received satellite signals from interference is a key issue for the receiver. To effectively cope with the coexistence of jamming and spoofing interference, and accurately suppress both jamming and spoofing, in this paper, a hybrid interference suppression algorithm based on robust beamforming is proposed. The combination of subspace projection algorithm, despreading algorithm, multiple signal classification (MUSIC) algorithm and robust beamforming based on the linearly constrained minimum variance criterion can suppress both jamming and spoofing effectively, and ensure that the desired signal is undistorted. At the same time, it can overcome the problem of inaccurate direction estimation under low signal-to-noise ratio. Numerical examples show that the proposed algorithm can effectively suppress hybrid interference.

In this paper, an efficient multi-beam transmission scheme that uses symbol-level precoding based on frequency diverse array (FDA) is proposed to enhance the physical layer security (PLS). Unlike the usual maximization of secrecy rate, we assume that the position information of passive eavesdropper (Eve) is not available at transmitter, which is a more realistic assumption. We use a minimum transmission message power criterion to design the precoder, subject to constraint on received signals at symbol level for per legitimate user (LU). This guarantees the valid reception of LUs to obtain the corresponding symbols under transmission messages power minimization. Then, after accurate calculation of the transmission message power, the remaining power can be allocated to artificial noise (AN), which deteriorates the quality of received signals at other regions. Numerical simulations show the validity and effectiveness of the proposed scheme.

Wide-band interference (WBI) is a critical issue for synthetic aperture radar (SAR), which may severely affect the imaging quality of SAR systems. To suppress WBI effectively, a novel interference suppression algorithm based on robust principal component analysis (RPCA) in time-frequency-pulse (TF-P) domain is proposed. For SAR echoes in TF-P domain, there are two useful properties: 1) The TF characteristic of useful signal in adjacent pulse are similar, indicating that useful signal has low-rank property; 2) Due to its variation of position and sparsely distrusted in TF-P domain, WBI has sparse characteristic. According to these properties, RPCA method is applied to decompose the TF-P matrix into a low-rank matrix (i.e. useful signal) and a sparse matrix (i.e. WBI). Finally, the WBIs can be reconstructed and subtracted from the echoes to realize the interference suppression. The experimental results of simulated data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.

The contest against electromagnetic spectrum are making the electromagnetic environment more and more congested. Synthetic aperture radar (SAR) requires larger bandwidth to obtain finer resolution, and thus inevitably affected by radio emitters sharing the same frequency band. Most of the radio frequency interference (RFI) originated from the terrestrial emitters, while there are also rare cases with interfering signals from space-borne satellites. In this paper, we analyzed the mechanism of terrain scattered interference (TSI) from space-borne RFI sources, and provide a case study of the interference signatures in Sentinel-1 data.

Directional modulation (DM), as an emerging promising physical layer security (PLS) transmission technique for wireless communications, has attracted much attention over the past decade. It endows transmitters the ability to directly transmit the confidential messages to legitimate receivers along with pre‐specified directions while distorting signal waveform signatures projected along all other spatial directions to guarantee the security of information transmission. Traditional DM designs are based on the assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. Nevertheless, it is not always the scenario in practical applications, as it is possible that Eves and LUs are in the same directions or even at the same positions, which results in that signals received by Eves will be approximately the same as LUs’. To address this problem, the chaos scrambling (CS) technique is employed in this paper. A DM technique based on CS and artificial noise (AN) is proposed for PLS enhancement. The symbol error rate, secrecy rate, and robustness of the proposed CS‐AN‐aided scheme are analysed and simulated. Simulation results show the effectiveness of the proposed method and the PLS can also be guaranteed even if Eves are aligning with the desired directions or very close to the LUs.

To overcome the shortcomings of fractal analysis in the time domain and Fourier transform domain, this letter mainly studies the joint fractal property of sea clutter of autoregressive (AR) spectrum and its application on weak target detection. Since the box-counting dimension is the most popular parameter to describe a fractal set and simply to calculate, we combined the box-counting dimension with AR spectrum estimate theory, which considers the correlation property of sea clutter series. Moreover, the intercept is regarded as an auxiliary feature for target detection. Then the box-counting dimension and intercept are used as a 2-D feature to analyze the joint fractal characteristic of AR spectrum, and a novel weak target detection algorithm is proposed based on the joint fractal characteristic of AR spectrum. In fact, radar target detection can be regarded as a binary-classification question, and the support vector machine (SVM) is applied to target detection. Finally, real S-band sea clutter data sets are analyzed. Compared to the traditional CFAR method and existing fractal methods, the proposed method improves the detection performance without complex computations.

In this paper, a frequency diverse array-based directional modulation with artificial noise synthesis scheme is proposed to enhance the physical layer security of wireless communications. We aim to optimize the secrecy performance by jointly optimizing the frequency offsets, the beamforming vector, and the artificial-noise projection matrix (ANPM). Specifically, we address the physical layer security problems for known locations of proximal eavesdropper (Eve) and legitimate user (LU). The beamforming vector and frequency offsets are designed to preserve the signal power at LU. The ANPM is calculated to minimize the effect of AN on LU. Furthermore, we extend our approach to the case of multi-LUs with unknown Eve locations. Being different from the case of a single LU, the frequency offsets across array antennas are optimized to equally allocate transmitted power to each LU. The numerical results show that the proposed method can provide a higher secrecy performance than conventional DM methods. In the case of multi-LUs with unknown Eve locations, the proposed method can provide a high secrecy capacity while achieving almost equal achievable capacity to each LU.

A new algorithm to estimate the direction of arrival (DOA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors is presented by exploiting the canonical polyadic decomposition (CPD) of tensors. In addition to spatial and temporal diversities, further information from the polarization domain is considered and used in this paper. Estimation errors of these parameters are evaluated by the Cramér-Rao lower bound (CRB) benchmark, in the presence of additive white Gaussian noise (AWGN). The superiority of the proposed algorithm is shown by comparing with the derivative algorithms of MUSIC and ESPRIT. In the proposed algorithm, the parameters can be estimated by virtue of the diversities of the spatial and polarization belonging to the factor matrices, rather than the conventional subspace which is the foundation of MUSIC and ESPRIT. Additionally, the classical CPD algorithm based on Alternating Least Squares (ALS) is introduced to verify the efficacy of the proposed CPD algorithm. Results demonstrate that when the number of snapshots is greater than 50, the proposed algorithm requires a smaller number of snapshots to achieve a high level of performance, compared against the subspace-based algorithms and the ALS-based algorithm. Furthermore, in the matter of the array with a small number of sensors, the discovered advantage concerning the Root Mean Square Error (RMSE) in estimating the DOA and the polarization state of the signal is noteworthy.

Narrow-band interference (NBI) and Wide-band interference (WBI) are common jamming signals against synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To effectively suppress NBI and WBI, a novel time-frequency iteration decomposition method is proposed based on ambiguity function iteration decomposition. In this algorithm, echoes contaminated by interferences are identified in the radon ambiguity function (RAF) domain. After that, the masked method and signal synthesis method are utilized to extract and recovery interferences from the ambiguity function. Finally, the reconstructed interferences are subtracted from the echoes, and the well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrates that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.

Wireless sensors network (WSN) is widely used in the Internet of Things at present. However, limited energy source is a critical problem in the improvement and practical applications of WSN, so it is necessary to improve the energy efficiency. As another important evaluation criterion of transmission performance, throughput should be improved too. To mitigate both of the problems at the same time, by taking the advantages of Time Division Multiple Access (TDMA) and Carrier Sense Multiple Access (CSMA) at the medium access control (MAC) layer of WSN, we propose a hybrid TDMA/CSMA MAC layer protocol. Meanwhile, we design a novel power control scheme to further reduce the energy consumption and optimize the transmission slots. The simulation results demonstrate that the proposed protocol significantly improves the throughput and energy efficiency.

Narrow-band interference (NBI) is a common jamming signal against synthetic aperture radar (SAR), in which the imaging quality can be degraded severely. To suppress NBI effectively, a novel interference suppression algorithm using robust principal component analysis (RPCA) based time-frequency signal separation is proposed. The RPCA algorithm is introduced for time-frequency signal separation for the first time. The experimental results of simulated and measured data demonstrate that the proposed algorithm not only can suppress interference effectively, but also preserve the useful information as much as possible.