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.

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.

A novel fast and adaptive method for synthetic aperture radar (SAR) superresolution imaging is developed. Based on the point scattering model in the phase history domain, a dictionary is constructed so that the superresolution imaging process can be converted to a problem of sparse parameter estimation. The approximate orthogonality of this dictionary is exploited by theoretical derivation and experimental verification. Based on the orthogonality of the dictionary, we propose a fast algorithm for basis selection. Meanwhile, a threshold for obtaining the number and positions of the scattering centers is determined automatically from the inner product curves of the bases and observed data. Furthermore, the sensitivity of the threshold on estimation performance is analyzed. To reduce the burden of mass calculation and memory, a simplified superresolution imaging process is designed according to the characteristics of the imaging parameters. The experimental results of the simulated images and an MSTAR image illustrate the validity of this method and its robustness in the case of the high noise level. Compared with the traditional regularization method with the sparsity constraint, our proposed method suffers less computation complexity and has better adaptability.

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.

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.

When assessing radar anti-jamming performance, the challenge of limited sample sizes is a significant hurdle. In response, this paper introduces a logistic fusion model that leverages Bayesian techniques and a Monte Carlo Markov chain (MCMC) sampling method based on a logistic regression model that characterizes the relationship between the signal-to-interference ratio (SIR) and the anti-jamming rate. The logistic curve’s inflection point and growth rate serve as crucial indices for evaluating radar anti-jamming performance, providing insights into the SIR threshold for successful jamming mitigation. The proposed model allows for the derivation of posterior distributions for these parameters using the MCMC sampling method and kernel density estimation. It also enables the fusion of anti-jamming data from multiple stages, including mathematical simulations, hardware-in-the-loop tests, and field tests. Through extensive simulations, our method achieves a remarkably low root mean square error (RMSE) of $0.0552$. Compared with a conventional BETA fusion model, our proposed logistic fusion approach demonstrates superior performance and robustness in accurately estimating the anti-jamming rate. The fusion of multi-stage data, even with varying levels of reliability, improves the overall accuracy of the performance evaluation.