This paper investigates the discrete-time sliding mode (DTSM) trajectory tracking control problem for fully actuated autonomous surface vehicles with stochastic network communication delays, based on a self-triggered mechanism. By integrating the Poisson distribution, Thiran approximation, and DTSM control methods, the adverse impact of stochastic network delays on trajectory tracking control is mitigated. To reduce data transmission load and the wear and energy consumption associated with frequent sensor sampling, this study explores enhancing the adaptability of the controller over a larger range of sampling periods. A novel adaptive DTSM power reaching law is proposed, facilitating the development of a DTSM controller that eliminates the need for repeated parameter adjustments across different sampling periods. This innovation enables effective trajectory tracking control over a broader range of sampling periods. Based on this, an uncommon non-predictive DTSM-based self-triggered control strategy is designed within the discrete-time domain. Unlike conventional prediction-based and non-predictive linear state feedback-based discrete-time self-triggered strategies, this method effectively balances computational complexity with the demands for robustness and rapid response. It eliminates the reliance on real-time system state monitoring required by event-triggered mechanisms. This is the first self-triggered strategy proposed to accommodate large sampling periods, thereby further reducing data transmission frequency while ensuring satisfactory trajectory tracking performance. Stability analysis demonstrates that all tracking errors converge to a small region near zero. Simulation results validate the efficacy of the proposed control strategy.

The distinguishability between background and anomaly is significant for accurate hyperspectral anomaly detection (HAD). The property that background and anomaly are characterized as signals with distinct differences in the frequency domain is of great value, however, existing HAD algorithms rarely consider this. In addition, deep learning (DL)-based methods that exploit reconstruction errors for HAD can inadvertently reconstruct anomalies alongside the background, leading to a high false alarm rate (FAR). To address these challenges, this study proposes a novel HAD model based on frequency-spatial–spectral domain analysis and criss-cross transformer (CCTrans), named F$\mathbf {S}^{\mathbf {2}}$CCTrans. Specifically, to suppress anomaly reconstruction, the frequency domain analysis paradigm is integrated into HAD, an advanced saliency map (SM) is constructed by analyzing the discriminative characteristics of anomaly and background in both amplitude and phase spectrum. The SM guides the model training in the direction of suppressing its capability to express and reconstruct anomalies, thereby obtaining a pure background estimate. To craft a superb background generator, the CCTrans network is designed that captures spatial-spectral features of the background in a very effective and efficient way. The CCTrans incorporates an ingenious criss-cross attention mechanism, which is focused on aggregating contextual information of all the pixels along its criss-cross path, thus significantly decreasing computational burden. Extensive comparisons with eight state-of-the-art methods on synthetic and real datasets demonstrate the superiority of F$\mathbf {S}^{\mathbf {2}}$CCTrans. Meanwhile, ablation studies reveal that the CCTrans requires about 68% less GPU memory and about 58% fewer FLOPs, highlighting its efficiency.

This paper investigates discrete-time sliding mode trajectory tracking control for fully actuated autonomous surface vessels (ASVs) with unknown nonlinear dead-zone and saturation inputs, utilizing a switching dynamic event-triggered mechanism (DETM). Through model integration, a direct relationship between ASV position and control inputs is established, simplifying trajectory tracking strategy design. ASVs face dead-zone and saturation constraints in control inputs, where low input signals may not overcome static friction, hindering maneuverability, and further increases are ineffective once actuators reach maximum thrust. Unlike linear dead-zone and saturation input constraints with known parameters, this paper considers a more realistic scenario of unknown nonlinearity, employing adaptive neural networks to approximate and compensate for the resulting unknown dynamics. Moreover, limited internal communication resources constrain real-time inter-subsystem communication in ASVs, while frequent short-period sampling in stable conditions results in unnecessary energy and computational consumption, collectively degrading trajectory tracking performance. A novel switching DETM is proposed to reduce unnecessary data transmission, which switches triggering conditions based on variations in auxiliary dynamic variables. Meanwhile, the controller output variation is integrated into the event-triggered conditions to enhance tracking control performance. Based on this, a discrete-time sliding mode trajectory tracking controller suitable for large sampling periods is designed. This ensures satisfactory tracking control effectiveness while further reducing unnecessary data transmission frequency and conserving limited communication resources within a larger range of sampling periods. All tracking errors are proven to be controlled within a small vicinity near zero. The numerical simulation results validate the efficacy of the proposed control strategy.

This paper investigates finite-time trajectory tracking control based on discrete-time sliding mode of underactuated surface vessels with compound disturbances comprising model parameter uncertainties and environmental disturbances under large sampling periods. By introducing the second-order Runge-Kutta method without complex operation to discretize the continuous-time vessel model, a high-precision discrete-time model is first obtained to ensure the controller design accuracy in discrete-time systems. Then, a novel finite-time discrete position tracking controller is developed by constructing a coordinate transformation to address the underactuating problem of surface vessels and convert position tracking error into expected velocity command. The compound disturbance is estimated and compensated by a high-order finite-time discrete disturbance observer. The current research on large sampling period control faces the shortcoming of adjusting parameters repeatedly to accommodate varying sampling periods while balancing convergence speed. To address it and enhance control system adaptability to large sampling periods while reducing operating losses and communication burdens on the sensing system, a novel adaptive reaching law is proposed based on existence conditions of the discrete-time sliding mode control system. Given this, a discrete-time sliding mode based finite-time velocity tracking controller is proposed to achieve stable velocity tracking over a large sampling period range. Finally, all tracking errors are demonstrated to converge within a finite time to a small region near zero. Two examples of comparative simulations validate the efficacy of the developed control strategy.

Remotely sensed hyperspectral images contain several bands (at about adjoining frequencies) for a similar zone on the surface of the Earth. Hyperspectral unmixing is a significant method for breaking down hyperspectral images into the components (endmembers) that conform each (potentially mixed) pixel and their abundance maps. Nonnegative matrix factorization (NMF) has attracted huge consideration because of the way that it can address mixed pixel scenarios. Most existing NMF unmixing techniques do not include spatial information in the analysis. An ongoing trend is to fuse the spatial and the spectral information contained in hyperspectral scenes to improve the solution. In this article, we build up another hyperspectral unmixing technique named spectral–spatial weighted sparse NMF (SSWNMF), in which two weighting factors are acquainted into the NMF model to upgrade the sparsity of the solution and capture the piecewise smooth structure of the data. We adopt a multiplicative iterative strategy to implement the proposed SSWNMF model. Our experimental results, conducted with both synthetic and real hyperspectral data, uncover that the proposed SSWNMF strategy can get accurate unmixing results over those gave by other unmixing strategies, with less parameter tuning.

Hyperspectral unmixing aims to decompose a hyperspectral image (HSI) into a collection of constituent materials, or end-members, and their corresponding abundance fractions. Recently, nonnegative tensor factorization (NTF)-based spectral unmixing methods have attracted significant attention owing to their outstanding performance when representing an HSI without any information loss. However, tensor factorization-based HSI methods do not fully exploit the spatial contextual information present in the scene. Besides, these approaches are sensitive to low signal-to-noise ratio (SNR) in HSIs. To address this limitation, we propose a new spectral-spatial weighted sparse nonnegative tensor factorization (SSWNTF) method to preserve the spatial details in the abundance maps via the spectral and spatial weighting factors. Our experiments with simulated data sets certified that the proposed method outperforms other advanced methods.