Zhengming Wang

Optical remote sensing techniques, particularly those relying on visible optical sensors, are critical for Earth observations. However, atmospheric haze severely degrades image quality owing to light scattering effects. These effects often lead to clouds in the observed images, resulting in partial image degradation. To address the challenge of non-homogeneous image dehazing, we propose a model-inspired deep unfolding network (MiDUNet). The architecture of MiDUNet is strictly derived and inspired by a novel alternating minimization algorithm. During algorithm design, traditional image regularization terms are replaced by several nonlinear functions to align with neural network processing, increasing flexibility and applicability. Afterward, a Multi-scale Spatial frequency Residual Block (MSRB) is used to capture complex scene structures by fusing multi-scale spatial information and frequency information. With these spatial-frequency features and a Multi-path Feature Enhancement Block (MFEB), local and global information are obtained, which increases the robustness to non-homogeneous dehazing and image texture preservation. Numerical experiments with remote sensing images and natural images demonstrate the competitive advantages of MiDUNet in non-homogeneous dense haze scenarios.

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios.

Cloud and edge computing paradigms are increasingly being applied to data processing for Internet of Things (IoT) sensors. Meanwhile, unmanned aerial vehicles (UAVs) can assist these sensor systems in data acquisition, especially in smart applications such as environmental monitoring and smart agriculture, where direct network connectivity for sensors is limited due to remote deployment. In this work, the Age of Information (AoI) is introduced for wireless sensor networks to measure the freshness of data information. We also develop a hierarchical UAV-assisted data processing framework to minimize AoI, where the multi-UAVs hover over the sensor clusters to collect data and conduct computing offloading by flexibly using the computation resources of edge server or cloud. Then, we innovatively propose a joint service association, trajectory scheduling and computing offloading mechanism for UAVs oriented by AoI. Specifically, we design a sensor clustering and sensor-hovering point (HP) association management scheme to improve the efficiency of data collection, and then propose an HP clustering model to establish the HP-UAV association. Further, a multi-objective optimization model is solved by devising a learning-based trajectory scheduling scheme. Simulation results show that the proposed ATSCO can not only converge well and improve the freshness of data information, but also realize superior performance than the mainstream schemes in various situations.

When evaluating the effectiveness of radar antijamming techniques, the limited sample size poses a challenge. To overcome this limitation, this paper proposes a Signal-toInterference Ratio Fusion (SIRF) model that integrates multi-stage data. The SIRF model is based on the relationship between the signal-to-interference ratio and radar target range, considering three fluctuation models for radar cross section: Swerling I-II, Swerling III-IV, and Rayleigh. By employing Bayesian theory and the anti-entropy weight method, the SIRF model enables the fusion of data collected from various stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Numerical results demonstrate that the proposed SIRF model exhibits superior consistency and robustness compared to the conventional fusion model based on the beta distribution.

Mobile edge computing (MEC) is a novel paradigm that offers numerous possibilities for Internet of Things (IoT) applications. In typical use cases, unmanned aerial vehicles (UAVs) that can be applied to monitoring and logistics have received wide attention. However, subject to their own flexible maneuverability, limited computational capability, and battery energy, UAVs need to offload computation-intensive tasks to ensure the quality of service. In this paper, we solve this problem for UAV systems in a 5G heterogeneous network environment by proposing an innovative distributed framework that jointly considers transmission assessment and task offloading. Specifically, we devised a fuzzy logic-based offloading assessment mechanism at the UAV side, which can adaptively avoid risky wireless links based on the motion state of an UAV and performance transmission metrics. We introduce a multi-agent advantage actor–critic deep reinforcement learning (DRL) framework to enable the UAVs to optimize the system utility by learning the best policies from the environment. This requires decisions on computing modes as well as the choices of radio access technologies (RATs) and MEC servers in the case of offloading. The results validate the convergence and applicability of our scheme. Compared with the benchmarks, the proposed scheme is superior in many aspects, such as reducing task completion delay and energy consumption.

Sliced Latin hypercube designs (SLHDs) are widely used in computer experiments with both quantitative and qualitative factors and in batches. Optimal SLHDs achieve better space-filling property on the whole experimental region. However, most existing methods for constructing optimal SLHDs have restriction on the run sizes. In this paper, we propose a new method for constructing SLHDs with arbitrary run sizes, and a new combined space-filling measurement describing the space-filling property for both the whole design and its slices. Furthermore, we develop general algorithms to search for the optimal SLHD with arbitrary run sizes under the proposed measurement. Examples are presented to illustrate that effectiveness of the proposed methods.

Boost-phase trajectory inference is one of the major objectives of the space-borne missile early-warning system. Traditional methods can be classified as either profile-based or profile-free methods. The profile-based methods are accurate but inadaptable to the types of missiles, whereas the profile-free methods are adaptable but inaccurate. To integrate the strengths of the profile-based and profile-free methods, a multimodel trajectory inference approach is proposed. First, a general net acceleration model (GNAM) containing only type-free prior information is constructed by the method of sieves. Then, a new kind of net acceleration profile is proposed by incorporating type-dependent prior information into the GNAM. After that, the multimodel approach is proposed following the Bayesian framework. Simulations indicate that the approach is accurate in estimation and capable for type identification.

Estimating the boost-phase trajectory of a ballistic missile using line of sight measurements from space-borne passive sensors is an important issue in missile defense. A well-known difficulty of this issue is the poor-observability of the target motion. A profile-based maximum penalised likelihood estimator is presented, which is expected to work in poor-observability scenarios. Firstly, a more adaptable boost-phase profile is proposed by introducing unknown parameters. Then, the estimator is given based on the Bayesian paradigm. After that, a special penalty for box constraint is constructed based on a mixed distribution. Numerical results for some typical scenarios and sensitivity with respect to a priori information are reported to show that the proposed estimator is promising.