In recent years, the integration of Unmanned Aerial Vehicles (UAV) into Wireless Sensor Network (WSNs) architectures has emerged as a promising solution for enhancing data collection efficiency. UAV-assisted data collection for WSNs should resolve the problem of energy-efficient communication between UAV and arbitrarily dispersed ground sensors. To enhance the reliability and efficiency of the entire system, an Optimal Topology Control and Energy-efficient Scheduling strategy (OTCES) for UAV-assisted data collection in WSNs is put forward. Based on the clustered-WSNs, a multi-objective model for data collection that minimizes the energy consumption of UAV and sensor nodes is established. Firstly, a density based spatial clustering of applications with noise algorithm is employed to group sensor nodes within the UAV’s effective communication range. Subsequently, to reduce the energy consumption of sensors and data transmission during UAV hovering, an improved deep deterministic policy gradient algorithm is proposed to address the issue of optimal UAV hovering positions and sensor’s transmission power. Then, to minimize the flight energy consumption of UAV, an ant colony optimization-inspired algorithm is designed to figure out the best traverse path. The results of simulation experiments show that compared with traditional methods, the proposed strategy has achieved remarkable results in terms of system energy consumption.

Cooperative localization is an essential task in multi-unmanned aerial vehicle (UAV) networks, especially in environments with partial Global Navigation Satellite System (GNSS) denial. By effectively integrating low-precision Inertial Navigation System (INS)-derived priors, inter-UAV relative distances, and sparse anchor-based measurements, the Extended Kalman Filter (EKF) can be employed for UAV localization. However, localization accuracy suffers under nonlinear dynamics and inaccurate knowledge of process and measurement noise statistics. To address these challenges, this paper proposes a Structurally-Integrated Attention Kalman Filter called SIA-KalmanNet for accurate and robust state estimation. By combining state-space models with attention-based neural networks in the Kalman flow, the estimator preserves the data efficiency and interpretability of traditional model-driven methods while implicitly learning second-order covariance matrices from data. Specifically, two networks are devised to learn the Kalman gain: the Self-Attention Residual Masked Network (SA-RMN) and the Self-Cross Attention Residual Masked Network (SCA-RMN). Among them, SA-RMN is a lightweight network for end-to-end state estimation. SCA-RMN introduces masked cross-attention to adaptively fuse forward state features and backward historical correction information to obtain a more precise state estimate. Furthermore, a practical training strategy based on sliding window and causal masking is developed to ensure efficient training and real-time inference. Experimental results show that the proposed algorithm achieves superior localization performance compared to the traditional EKF and KalmanNets. Moreover, it exhibits strong robustness against INS drift and non-line-of-sight (NLOS)-induced distance errors, even in harsh environments where only a limited number of UAVs can access GNSS signals.

The implementation of the United Nations (UN) 2030 Agenda for Sustainable Development (2030 Agenda), with its 17 sustainable development goals (SDGs), faces challenges such as insufficient data, limited research methodologies, and uneven progress across regions. Earth observation (EO), particularly scientific satellites, offers unique advantages in supporting global sustainable development by providing objective, dynamic, and large-scale datasets for SDG evaluations and policymaking, as well as by facilitating the study of Earth’s environmental systems and their interactions with human activity. Sustainable Development Science Satellite 1 (SDGSAT-1), the world’s first scientific satellite dedicated to supporting the 2030 Agenda, was designed and developed by the International Research Center of Big Data for Sustainable Development Goals (CBAS). Its three advanced EO sensors, i.e., Thermal Infrared Spectrometer (TIS), Glimmer Imager (GLI), and Multispectral Imager (MSI), furnish high-quality data, enabling continuous monitoring of human activity and environmental changes to bolster SDG-related research and global sustainability initiatives. As of November 2025, SDGSAT-1 has collected over 480 000 global terrain coverage images since its launch in November 2021. All its datasets have been shared free of charge with the Global Scientific Community through the SDGSAT-1 Open Science Program initiated in September 2022. The datasets have enabled researchers from more than 110 countries, 10 UN agencies, and various international organizations to publish over 180 scientific articles, 17 UN reports, and numerous public data products. These have demonstrated applications in urban development, disaster response, environmental monitoring, agriculture, and marine conservation. This article reviews the technical innovations and mission specifications of the SDGSAT-1 satellite, demonstrates its contribution in leveraging space technology for SDG monitoring and evaluation, and discusses the future evolution of development of EO systems, specifically the planned Sustainable Development Satellite Constellation, for supporting the global achievement of SDGs.

Given the fact that visual-inertial odometry (VIO) is faced with the challenges of localization drift in the long run, we utilize drift-free Ultra-Wideband (UWB) measurements to eliminate accumulated errors in VIO. Existing UWB-VIO fusion methods are mostly constrained by the accuracy of prior UWB anchor positions. However, in large-scale localization scenarios, the precise locations of UWB anchors are difficult to obtain, and the offline calibration process is complex, significantly limiting flexibility. In this paper, we firstly design a lightweight initialization method based on a dual sliding window structure, which can rapidly obtain initial guesses for the UWB anchor coordinates. After that, we further propose a joint estimation system to refine the anchor coordinates while estimating the correction for VIO. The system combines filter-based and optimization-based methods, which mainly consists of an initialization module and a nonlinear estimator module. The filter in the initialization module provides optimization initial values and covariances, and mutually, the optimization results from the nonlinear estimator provide priors for the filter. Finally, the performance of our proposed approach is verified through both public datasets and real-world experiment. Our project, along with our dataset, has been open-sourced in the form of ROS package and ROS bag.

The electromagnetic spectrum serves as a critical resource in advanced fields such as wireless communications, radar detection, and electronic warfare. The Frequency Assignment Problem (FAP) represents a complex optimization issue, encompassing the rational allocation of limited frequency resources to minimize electromagnetic interference (EMI), characterized by multiple objectives, intricate constraints, and dynamic characteristics. Traditionally, FAP is often perceived to a static dimension problem with one-to-one correspondence between equipment and frequencies. However, introducing flexible frequency handoff in the time-domain, it evolves into a one-to-many uncertain relationship, resulting in dynamic variable dimensions of FAP. To address the challenges, this paper create a cross-swarm dimension mutation strategy to generate heterogeneous multi-swarms with diverse variable structures. The life-cycle is set for mutated swarms to implement elimination mechanism, actualizing dynamic adjustment for quantity of swarms. A Dynamic Heterogeneous Multi-Swarm PSO (DHMSPSO) is proposed, which provides a new perspective for solving optimization problems with dynamic variable dimension, and be applied to develop multi-objective frequency assignment algorithm. Ultimately, through simulation experiments on FAP, comparative analysis and statistical tests between DHMSPSO and other optimization algorithms validate the feasibility and superiority of DHMSPSO. And futher simulation for elimination mechanism demonstrates that can prevent excessive swarm expansion and improve the optimization efficiency.

The unmanned aerial vehicle (UAV) formation reconfiguration problem, due to its various limitations and high nonlinearity, has become a significant challenge in UAV formation. The existing methods mainly convert the problem into a constrained global optimization problem using control parameterization and time discretization (CPTD). This method, however, overlooks possible constraints on global optimization and the fact that solving the optimization problem itself can be difficult and time-consuming. In this paper, we firstly comprehensively considered the constraints in the UAV formation reconfiguration and proposed a problem model closer to real-world conditions. Then, we propose a new heuristic algorithm, the enhanced marine predators algorithm (EMPA) to address the constrained optimization problem, which has stronger optimization capability and greater stability when solving high-dimensional nonlinear problems. Finally, to decrease the computation cost, a more accurate and real-time receding horizon controller is designed based on the proposed EMPA, where the global optimal control problem of UAV formation reconfiguration is decomposed into a series of online local optimization problems. Simulation experiments verify the feasibility and effectiveness of the proposed control method.

A novel method of building damage extraction using bitemporal spectral and height data was proposed in this study. The pre-event height data were first estimated from the pre-event spectral image using Conditional Generative Adversarial Net (cGAN). The pre- and post-event spectral and height data were then used in extraction of building damages with significant changes in height and homogeneity. The experimental results demonstrated that the proposed method outperformed the existing method.

Recently, human behavior sensing based on WiFi channel state information has drawn more attention in the ubiquitous computing field because it can provide accurate information about the target under a device‐free scheme. This paper concentrates on user authentication applications using channel state information. We investigate state‐of‐the‐art studies and survey their characteristics. First, we introduce the concept of channel state information and outline the fundamental principle of user authentication. These systems measure the dynamic channel state information profile and implement user authentication by exploring the channel state information variation caused by users because each user generates unique channel state information fluctuations. Second, we elaborate on signal processing approaches, including signal selection and preprocessing, feature extraction, and classification methods. Third, we thoroughly investigate the latest user authentication applications. Specifically, we analyze these applications from typical human action, including gait, activity, gesture, and stillness. Finally, we provide a comprehensive discussion of user authentication and conclude the paper by presenting some open issues, research directions, and possible solutions.

In the multisensor target tracking system, the key of the target tracking performance depends on the state estimation accuracy to a great extent. However, the system uncertainties will seriously affect the performance of the state estimation. Up to now, little research focuses on the state estimation for the multi‐sensor hybrid target tracking systems with multiple uncertainties including the multiple models, the unknown inputs and the systematic biases. In this study, the minimum error entropy based on the multiple model estimation for the multisensor hybrid uncertain target tracking systems with the multiple system uncertainties is presented. The minimum variance unbiased filter based on the general systematic bias evolution model decoupled with the unknown state is designed to estimate the optimal systematic biases and compensate the system measurements. Taking full advantage of the compensated measurement information in time and space, the multiple model observer based on the minimum error entropy is designed to obtain the optimal state estimation. The simulation results of the target tracking scenario illustrate the effectiveness of the proposed method, and the indoor target tracking and positioning experiment based on the ultrawideband further verifies that the proposed method is satisfying.

The wide-sense auto-regressive moving-average (ARMA) model is widely applied into varieties of fields. The unknown bounded parameter estimation of an ARMA model is an extremely vital research subject. Up to recent, most research is conducted with the known disturbing environment noise or the model of the known noise with the unknown variance. Actually the disturbing noise in the modern control system is really complex and unknown. To the best of our knowledge, less attention on the unknown boundary parameter estimation for the wide-sense stationary hidden ARMA process with unknown noise is paid. In this paper, a dual particle filter-based method to estimate the state and unknown bounded parameter jointly for the hidden wide-sense ARMA processes under the unknown noise is presented, which includes two steps. In the first step, the kernel smoothing particle filter algorithm is utilized to estimate the unknown bounded ARMA model parameter. And sufficient statistics based on Beta distribution is utilized to approach the posterior distribution of the parameter. In the second step, the particle filter algorithm is utilized to estimate the state of an ARMA model with the model parameter obtained in the first step. For the noise model is extremely unknown, the Gaussian mixture model is adopted to approach the posterior probability function in the EM algorithm. Simulation results verify the effectiveness of the proposed scheme.

In multi‐detection (MD) systems including over‐the‐horizon radar (OTHR) systems, the forward‐based receivers systems and passive coherent location systems, resolvable multiple detections of one target due to the multi‐path propagation will be received, in which both identification (including measurement association and propagation mode/transmitting origin identification) and estimation (including path‐conditional state estimation and multi‐path track fusion) are deeply coupled and affect each other. Up to present, all corresponding methods fall into the scope of sequential identification orientated or estimation orientated processing, and hence the solutions are not satisfactory. Here the joint identification and estimation scheme for the MD systems (MD‐JIE) is developed based on the generalized Bayes risk considering both identification and estimation performance. The likelihood‐ratio function and the conditional probability density function as the identification cost and estimation cost are recursively calculated, respectively. Taking the data‐association constraints into consideration, the proposed MD‐JIE scheme is implemented via online constrained optimization technology. Compared to the identification‐then‐estimation (ITE) method and the estimation‐then‐identification (ETI) method, the proposed MD‐JIE method prevails in the joint performance measure with the higher correct identification rate than the ETI method and the smaller root MSE than the ITE method. Besides, the robustness of the proposed method is verified.

A ring topology is a common solution of network-on-chip (NoC) in industry, but is frequently criticized to have poor scalability. In this paper, we present a novel type of multi-ring NoC called isolated multi-ring (IMR), which can even support chip multiprocessors (CMPs) with 1,024 cores. In IMR, any pair of cores are connected via at least one isolated ring, so that each packet can reach the destination without transferring from one ring to another. Therefore, IMR no longer needs expensive routers as mesh, which not only enhances the network performance but also reduces hardware overheads. We utilize simulated evolution to design optimized IMR topologies. We compare these IMR topologies against nine representative NoCs (e.g., traditional mesh, multi mesh, low-cost mesh, Express-virtual-channels mesh (EVC), torus ring, and hierarchical ring). We observe from experiments that IMR significantly outperforms its competitors in both saturation throughput and latency across all scenarios considered. For example, in a 16 × 16 CMP, IMR improves the saturation throughput of a state-of-the-art mesh (EVC) by 265.29 percent on average, and reduces the average packet latency on SPLASH-2 application traces by 71.58 percent, while consuming 5.08 percent less area and 9.76 percent less power. In a 32 × 32 CMP, IMR averagely improves the saturation throughput of EVC by 191.58 percent, and averagely reduces the packet latency on SPLASH-2 application traces by 23.09 percent, while consuming 2.86 percent less area and 10.81 percent less power.

A widely adopted methodology for verifying the memory subsystem of a Chip Multiprocessor (CMP) is to verify executions of parallel test programs on the CMP against the given memory consistency model, which has been long known to be time consuming in both theory and practice. To accelerate memory consistency verification, previous approaches have to bear the cost of availability (e.g., relying on dedicated hardware supports that have not been offered by many commodity CMPs) or completeness (e.g., missing some bugs). In the meantime, the impact of parallel programs on memory consistency verification has more or less been overlooked. One piece of evidence is that few investigations have been dedicated to finding appropriate test programs enabling more efficient verification From a novel perspective of test program, we devise a practical technique called “program regularization,” which can effectively reduce the computation time of memory consistency verification. The key intuition behind program regularization is that any parallel program, if being reformed appropriately, can enable efficient memory consistency verification. More specifically, for an original program, program regularization introduces some auxiliary memory addresses, and periodically inserts load/store operations accessing these addresses to the original program. With the regularized program, memory consistency verification can be accomplished in linear time (with respect to the number of memory operations) when the number of processors is fixed. Experimental results show that program regularization can significantly accelerate memory consistency verification. Last but not least, our technique, which does not rely on concrete verification algorithm or dedicated hardware support, can be smoothly integrated into existing presilicon/postsilicon verification platforms of industrial CMPs to speed up memory consistency verification.