Unmanned aerial vehicle (UAV) communication systems usually operate in harsh scenarios, which require accurate information about the topology and wireless channel to achieve the desired transmission performance. Therefore, when millimeter-wave (mmWave) communication with its intrinsic Line-of-Sight (LoS) condition is adopted, accurate target localization is essential to determine the spatial relationship between the UAV and the grounded receivers (Rxs). In this article, a computer-vision (CV)-aided jointly optimization scheme of flight trajectory and power allocation is designed for mmWave UAV communication systems by utilizing the visual information captured via cameras equipped at the UAV. Compared with traditional schemes, the implementation cost and overhead can be greatly saved as no radio frequency transmissions are required in the proposed localization scheme. In addition, the transmit power at the UAV is jointly optimized with its flight trajectory in two different cases. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.

The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 d of heavy pollution observation experiments in Beijing (China), as well as an exhaustive evaluation of the gradient, wavelet covariance transform, and cubic root gradient methods, a novel algorithm based on the cluster analysis of the gradient method (CA-GM) of lidar signals is developed to capture the multilayer structure and achieve night-time stability. The CA-GM highlights its performance compared with radiosonde data, and the best correlation (0.85), weakest root-mean-square error (203 m), and an improved 25 % correlation coefficient are achieved via the GM. Compared with the 39 d experiments using other algorithms, reasonable parameter selection can help in distinguishing between layers with different properties, such as the cloud layer, elevated aerosol layers, and random noise. Consequently, the CA-GM can automatically address the uncertainty with multiple structures and obtain a stable NBLH with a high temporal resolution, which is expected to contribute to air pollution monitoring and climatology, as well as model verification.

Due to the complexity of airborne remote sensing scenes, strong background and noise interference, positive and negative sample imbalance, and multiple ship scales, ship detection is a critical and challenging task in remote sensing. This work proposes an end-to-end anchor-free oriented ship detector (AF-OSD) framework based on a multi-scale dense-point rotation Gaussian heatmap (MDP-RGH) to tackle these aforementioned challenges. First, to solve the sample imbalance problem and suppress the interference of negative samples such as background and noise, the oriented ship is modeled via the proposed MDP-RGH according to its shape and direction to generate ship labels with more accurate information, while the imbalance between positive and negative samples is adaptively learned for the ships with different scales. Then, the AF-OSD based on MDP-RGH is further devised to detect the multi-scale oriented ship, which is the accurate identification and information extraction for multi-scale vessels. Finally, a multi-task object size adaptive loss function is designed to guide the training process, improving its detection quality and performance for multi-scale oriented ships. Simulation results show that extensive experiments on HRSC2016 and DOTA ship datasets reveal that the proposed method achieves significantly outperforms the compared state-of-the-art methods.

The remote sensing of water clouds is useful for studying their spatial and temporal variations and constraining physical processes in climate and weather prediction models. However, radar-only detection provides inadequate information for the cloud droplet size distribution. Here, we propose a novel lookup-table method, which combines lidar (1064, 532 nm) and radar (8.6 mm) to retrieve profiles of cloud optical (backscatter coefficient and extinction coefficient) and microphysical properties (effective diameter and liquid water content). Through the iteration of the extinction-to-backscatter ratio, more continuous cloud optical characteristics can be obtained. Sensitivity analysis shows that a 10% error of the lidar constant will lead to a retrieval error of up to 30%. The algorithm performed precise capture of the ideal cloud signal at a specific height and at full height and the maximum relative error of the backscatter coefficients at 1064 nm and 532 nm were 6% and 4%, respectively. With the application of the algorithm in the two observation cases on single or multiple cloud layers, the results indicate that the microphysical properties mostly agree with the empirical radar measurements but are slightly different when larger particles cause signal changes of different extents. Consequently, the synergetic algorithm is capable of computing the cloud droplet size distribution. It provides continuous profiles of cloud optical properties and captures cloud microphysical properties well for water cloud studies.

Complexly distributed aerosol particles have significant impacts on climate and environmental changes. As one of the vital atmospheric power sources, the wind field deeply affects the distribution and transport of aerosol particles. For a more comprehensive investigation of the aerosols flux and transport mechanism, two retrieval methods of aerosol optical properties (backscatter coefficient and extinction coefficient at 1550 nm) from coherent Doppler lidar (CDL) observation are proposed in this paper. The first method utilizes the calculated aerosol backscatter coefficient (532 nm) from Mie-scattering lidar datasets and the iterative Fernald method to retrieve aerosol optical property profiles during joint measurements with CDL and Mie-scattering lidar. After verifying the correctness of the first method compared with AERONET datasets, we proposed the second retrieval method. Using the forward integral Fernald method with near-ground reference aerosol extinction coefficient calculated by atmospheric visibility, aerosol optical properties at 1550 nm could be obtained. Thirty-six-day joint measurements with two lidars were specially designed and conducted to verify the correctness of these retrieval methods. The validation results of these two methods indicate great performances, where the mean relative errors are 0.0272 and 0.1656, and the correlation coefficients are 0.9306 and 0.9197, respectively. In conclusion, the feasibility of these two retrieval methods extends the capability of CDL to detect aerosol optical properties and also provides a possibility to observe the aerosol distribution and transport process comprehensively, which is a great promotion of aerosol transport studies development.

Satellite-to-ground communication systems typically operate in environments with high-interference levels, complex topologies, and stringent platform constraints. Therefore, intelligent, anti-interference, and low-power systems are required to achieve the desired transmission performance. This article proposes a system for optimizing high-frequency satellite-to-ground communications using computer vision (CV) technology, like millimeter-wave (mmWave) satellite communication systems. The system uniquely combines CV-based target localization with adaptive beamforming and power control to optimize communication links with ground targets, such as base stations, ships, and aircraft. This approach significantly outperforms traditional radio frequency-based methods in accuracy and efficiency, particularly in dynamic mmWave scenarios. Simulation results confirm the superiority of our system in terms of sum rate and energy efficiency, demonstrating its potential to revolutionize high-frequency satellite communications by providing reliable, high-quality service to terrestrial targets. Finally, simulation results are presented to demonstrate the efficiency of the proposed schemes.