Recently, graph convolutional network (GCN) has received more and more interest in the field of hyperspectral image classification (HSIC). The existing GCN-based models for HSIC propagate and aggregate information through the GCN network based on the graph, which is constructed according to spatial location or spectral similarity. However, the constructed graph may not be ideal for the downstream classification task due to the variety of spectral characteristics. In this paper, a fully connected graph is adaptively constructed to make full use of local spatial information and global spectral information. Besides, we apply a neural sparsification technique to remove potentially task-irrelevant edges in case of misleading message propagation. Furthermore, label propagation (LP) serves as regularization to assist the graph network in learning proper edge weights that lead to improved classification performance. The resulting network is end-to-end trainable. The experimental results on three popular benchmarks, including Indian Pines, Pavia University, and Kennedy Space Center, demonstrate the superiority of our algorithm.

Graph neural network (GNN) has recently gained increasing attention in the hyperspectral image (HSI) classification. Compared with convolutional neural network (CNN), GNN can effectively relieve the scarcity of labeled data. In our method, we first perform feature learning on large-scale irregular regions through GNN and then extract local spatial–spectral features at the pixel level. Besides, we incorporate edge convolution (EdgeConv) into GNN to adaptively capture the interrelationship of the representative descriptors and fully exploit the discriminative features on graph. Experiments on several HSI datasets show that our method can achieve better classification performance compared with the state-of-the-art HSI classification methods.

This paper details a method to improve accuracy of the land vehicle attitude estimation. It employs the vehicle model constraints to enhance the integration of GPS and a low-cost MEMS inertial measurement unit (MIMU) (GPS/MIMU). To improve the yaw angle estimation, we propose a lateral velocity constraint (LVC) aided method based on the observability analysis for the integrated fusion system. The theoretical analysis indicates that the yaw angle will be directly observed if LVC is augmented into the GPS/MIMU fusion algorithm. Furthermore, for LVC/MIMU system without GPS, the roll angle is always well estimated regardless of whether the vehicle is moving or stationary, and, surprisingly, the pitch angle can also be relatively accurately estimated if the vehicle is moving. Thus, when GPS is unavailable, the LVC/MIMU fusion is proposed by replacing the GPS measurement with the virtual measurement LVC. It is an encouraging improvement since the accumulating errors induced by gyro bias can be suppressed using MIMU alone. Moreover, to overcome the shortage that the pitch angle is not observed if the vehicle is stationary, we propose to fuse the gyros with gravity-based angles by Kalman filtering if the vehicle is detected motionless based on a switching rule. The experimental results compare favorably to theoretical analysis, showing that the proposed method can effectively improve the accuracy for land vehicle attitude estimation.

In this paper, we design robust downlink beamforming against the imperfect channel state information (CSI) for beam division multiple access (BDMA) massive multiple-input multiple output (MIMO) systems. Following a worst-case deterministic model, the proposed design is formulated as minimizing the power consumption of base station (BS) under different signal-to-interference-plus-noise ratio (SINR) constraints. The S-Procedure and semi-definite relaxation (SDR) are used to convert the initial non-convex optimization to a convex semi-definite programming problem. Then the optimality of SDR is strictly proved by showing the rank-one property of the optimal beamforming thanks to the orthogonal channels under BDMA scheme. More importantly, we make one step further by deriving the optimal beamforming directions and optimal beamforming power allocation of the SDR in closed-form, which greatly reduces the optimization complexity and makes the proposed design practical for a real word massive MIMO system. Simulation results are then provided to verify the efficiency of the proposed robust beamforming algorithm.

The performance of low‐density parity‐check (LDPC) code is analysed in the frequency hopping (FH) system with partial‐band interference (PBI). Both of Slow Frequency hopping (SFH)/binary phase‐shift keying (SK) and Fast Frequency hopping (FFH)/binary frequency‐SK schemes are considered in which the channel fading and diversity combining are involved, respectively. The symmetry conditions on PBI channel are proven, and then the probability densities of initial message for sum–product decoder are derived. Given a probability of hopping to a jammed state, the decoding thresholds of LDPC codes on PBI channel are obtained using density evolution, which are in good agreement with the simulation results. Furthermore, the irregular LDPC codes are optimised over PBI channel, and the simulation result shows that it has better anti‐jamming performance than the existing regular and irregular codes which have been employed in FH systems.

In this paper, we design transmit beamforming for a full duplex base station (FD-BS) considering both self-interference mitigation and physical-layer security. The proposed design is formulated as minimizing the power consumption of FD-BS under different signal-to-interference-and-noise-ratio (SINR) constraints. Semidefinite relaxation (SDR) is used to convert the initial nonconvex optimization to be a convex semidefinite programming (SDP) problem. Then the optimality of SDR is strictly proved by showing the existence of the rank-one optimal solutions. To reduce the computational complexity, we develop zero forcing beamforming-based suboptimal algorithms, where the solutions can be obtained using golden search and closed-form solutions can be derived in each step. Simulation results are then provided to verify the efficiency of the proposed algorithms.

In this paper, we design transmit beamforming for a full-duplex base station (FD-BS) considering joint self-interference mitigation and physical-layer security enhancement. The proposed designs are formulated to minimize the power consumption of FD-BS, under different signal- to-interference-and-noise-ratio (SINR) constraints. We strictly prove the optimality of SDR by showing the existence of rank-one solutions. Simulation results are provided to demonstrate the efficiency of the proposed algorithms.

2-D principal component analysis based on l1 -norm (2DPCA-L1) is a recently developed approach for robust dimensionality reduction and feature extraction in image domain. Normally, a greedy strategy is applied due to the difficulty of directly solving the l1 -norm maximization problem, which is, however, easy to get stuck in local solution. In this paper, we propose a robust 2DPCA with non-greedy l1 -norm maximization in which all projection directions are optimized simultaneously. Experimental results on face and other datasets confirm the effectiveness of the proposed approach.

In this paper, we design joint information beam-forming and jamming beamforming to guarantee both transmit security and receive security for a full duplex base station (FD-BS). Specifically, we aim to maximize the secret transmit rate while constrain the secret receive rate to be greater than a predefined bound. We convert the original non-convex problem into a new sequence of subproblems where the semidefinite programming (SDP) relaxation can be applied to efficiently find the optimal solutions. We strictly prove that such a relaxation does not change the optimality for these subproblems. Then the global optimal solutions of the original non-convex problem can be obtained via a one-dimensional search. Simulation results are provided to verify the efficiency of the proposed algorithms.

An improved quadrature nonlinear filter named delayed Genz-Keister sequences-based sparse-grid quadrature filter (DGKSGQF) is developed for the target tracking problems. The filter changes the non-nested Gaussian quadrature points of the quadrature filters to the nested Genz-Keister points for selecting the unvariate points, which are the basis point sets extended to form a multidimensional grid using the sparse-grid theory. As a result, the points used for lower accuracy levels DGKSGQF can be reused for any higher accuracy level. Thus, it can further reduce the number of total points used for the conventional Gauss-Hermite SGQF without sacrificing performance. The proposed filter is applied to the reentry ballistic target tracking problem. The simulation results show that the DGKSGQF achieves higher accuracy than the EKF and the UKF. In addition, it can more flexibly control the performance in terms of the number of points and accuracy level.

This paper presents a method using the Global Positioning System (GPS)-measured course angle to improve the accuracy of the vehicle attitude estimation for low-cost inertial navigation system/GPS (INS/GPS) integration. Observability properties of the error states in the low-cost integration navigation system are first analyzed, indicating that the attitude estimation is severely affected by vehicle maneuvers, particularly the yaw angle. The pitch and roll angles are strongly observed; hence, the observability of these two angles is nearly free of influence caused by vehicle maneuvers, and these two angles can be accurately estimated. To improve the yaw-angle estimation, we propose a cascaded Kalman filter to deal with the yaw angle separately with the aid of the GPS-measured course angle. Additionally, two switching rules are established to remove the influence caused by the sideslip angle and GPS noise. The experimental results validate the observability analysis of the low-cost INS/GPS system and show that the proposed attitude estimation method can effectively improve the accuracy of the vehicle attitude estimation, suggesting that this technique is a viable candidate for many control applications used in cars.

In this letter, a stable and orthonormal version of the OJA algorithm (SOOJA) is investigated for principal and minor subspace extraction and tracking. The new algorithm presented here guarantees the orthonormality of the weight matrix at each iteration through a novel orthonormalization method. Moreover, it obtains both a high numerical stability and a low computational complexity. The superiority of the proposed algorithm to some existing subspace tracking algorithms is demonstrated using a classical example. Simulation results confirm the veracity of the subspace tracking algorithm advocated.

A closed-form array response estimation (CARE) technique for blind source separation in wireless communication is developed. By exploiting the data structure of second-order statistics of the array output in the presence of multipath, we construct a signature matrix in such a way that its eigenvectors corresponding to non-zero eigenvalues are just the array response vectors. Thus a closed-form solution of array response can be obtained by eigen-decomposition. The theoretical analysis and the simulations show that the proposed method achieves array response estimation with little constraint on signal property and propagation environment such as scatters or angular spread. Moreover, the array considered here can be of arbitrary geometry and even uncalibrated.

This paper presents a new cyclic direction-of-arrival (DOA) estimation algorithm. By exploiting a new minimum-redundancy linear array (MRLA) configuration and cyclostationarity, the proposed algorithm constructs a matrix pencil from the pseudo-data matrix of 2N-1 virtual sensors, which are the results of temporal and spatial processing with designed M-sensor Sum-MRLA. Theoretical analysis and computer simulations show that, for each cyclic frequency, the algorithm can estimate 2N-2 DOAs with M-sensor Sum-MRLA (N>M).

In smart antenna systems (SAS), the most compelling research work is the space-division multiple-access (SDMA) which includes uplink source separation and downlink selective transmission. We propose a subspace-based spatial signature estimation algorithm of cochannel multiuser signals by jointly exploiting cumulant and cyclostationarity, and apply it to multiuser signal copy in uplink SDMA. Our method constructs a new matrix called the spatial signature matrix (SS matrix), and estimates the multiuser spatial signature through the eigen-decomposition of the SS matrix. Based on estimated spatial signature, a spatial filter bank is designed for cochannel multiuser waveform estimation. Computer simulations show that the spatial response of the filter bank can be used to find the direction-of-arrival (DOA) of the correlated multiuser signals.