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.

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.

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.

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.

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.

Abstract In this study, a novel fast wideband direction of arrival (DOA) estimation algorithm is proposed to reduce the computational complexity. First, a multiple measurement vector (MMV)‐based compact structure for a wideband signal is established. Combined with the focus operation, the array manifolds of different frequency bins are transformed into the dictionary of the reference frequency. Then, two efficient novel methods named adaptive step‐size‐based null space tuning with hard thresholding and feedback (ASNHF) and MMV‐ASNHF are proposed to process single measurement vector and MMV problem, respectively. Finally, wideband DOA estimation can be achieved by MMV‐ASNHF algorithm. Compared with other algorithms, the proposed algorithm has higher accuracy at a low signal‐to‐noise ratio and lower computational complexity. Simulation results show that the proposed estimator is effective and feasible.

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.

In synthetic aperture radar (SAR) system, the separation and reconstruction of useful signal from Narrow-band interference (NBI) and Wide-band interference (WBI) components is a challenging problem. In this paper, a novel time-varying interference suppression algorithm is proposed based on the signal synthesis from joint time-frequency (TF) distribution. This algorithm makes full use of two TF representations: Wigner distribution (WD) and cross WD (CWD). After cross-terms elimination, these two TF representations are equal or close to the sum of WDs or CWDs of individual signal components, respectively. Based on this property, interferences can be separated and reconstructed by matrix rearrangement and eigenvalue decomposition (EVD). Compared with the traditional SSM (TSSM), the proposed algorithm has two advantages: 1) it is more accurate, since it avoids the approximate interpolation to WD; 2) it is quite time-saving, due to its matrix obtained by fast Fourier transform (FFT) and matrix rearrangement instead of the discrete Fourier transform (DFT). Experimental results demonstrate the effectiveness of the proposed approach in terms of accuracy and computational complexity.

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.

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.

Frequency diverse array- (FDA-) based directional modulation (DM) is a promising technique for physical layer security, due to its angle-range dependent transmit beampattern. However, the existing schemes are not suitable for the broadcasting scenario, where there are multiple legitimate users (LUs) to receive the confidential message. In this paper, we propose a novel random frequency diverse array- (RFDA-) based DM scheme to realize the point to multi-point broadcasting secure transmission in both angle and range dimension. In the first stage, the beamforming vector is designed to maximize the artificial noise (AN) power, while satisfying the power requirement of LUs for transmitting the confidential message simultaneously. In the second stage, the AN projection matrix is obtained by maximizing signal-to-interference-plus-noise ratio (SINR) at the LUs. The proposed scheme only broadcasts the confidential message to the locations of LUs while the other regions are covered by AN, which promotes the security of the wireless broadcasting system. Moreover, it is energy efficient since the power of each LU is under accurate control. Numerical simulations are presented to validate the performance of the proposed scheme.

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.

Multiple informative polarimetric descriptors can be computed from direct measurements of polarimetric covariance matrix and target decomposition theorems. Under the tensor algebra framework, each pixel is modeled as a third-order tensor object by combining multi-features and incorporating neighborhood spatial information together. Typically, the tensor object is of high correlation and redundancy in both the spatial and feature dimensions. In this paper, we propose a feature extraction method using the multilinear principal component analysis to facilitate the classification process. Experimental results in comparison with principal component analysis, independent component analysis and linear discriminate analysis demonstrate that the classification accuracy is significantly improved since the extracted features by the proposed method are more discriminative.

In the aforementioned letter, the authors indicate that with a$2M + 1$sensor uniform linear array (ULA), up to$2M - 1$sources can actually be localized by the proposed algorithm. In this comment, however, we prove that the algorithm will no longer be valid if the number of sources exceeds$M$. A numerical simulation is performed to verify this conclusion.