Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study’s methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations.

Currently, the angle regression-based methods dominate the field of rotating object detection. However, this type of method faces two obvious limitations: i) The boundary problem caused by the angle prediction beyond the angle definition domain; and ii) the Intersection over Union (IoU) of two rotating rectangles is not derivable to the angle, which forms a bottleneck in the improvement of detection accuracy. Addressing above issues, this paper proposes a non-angle-regressed rotating object detection framework, and the core idea of which consists of two points: i) Introducing a rotating object representation that avoids angle to ensure that the regression parameter never cross the boundary, thus effectively solving the boundary problem; and ii) designing a simple and derivable loss function based on this representation that can replace the rotating IoU to a certain extent, which is capable of improving the detection accuracy. The proposed method is tested on a large-scale aerial remote sensing dataset. Without bells and whistles, it easily outperforms the state-of-the-art angle regression-based rotating object detection method, improving the $\mathbf{mAP}_{50}$ by 0.3%.

This paper aims to investigate a collaborative localization and guidance method for a multi-heterogeneous unmanned ground vehicles (UGVs) to address the last-mile delivery problem in e-commerce. Firstly, this paper examines the use of Ultra-Wideband (UWB) technology to provide localization services for UGVs. By aggregating three UWB base stations into a group and deploying multiple clusters of UWB base stations using a dynamic particle swarm optimization algorithm, the interested area can be covered. Secondly, this paper transforms the cooperative guidance problem of multiple UGVs into an optimization problem, combining the Floyd algorithm and the Particle Swarm Optimization (PSO) algorithm as a heuristic algorithm for task allocation and path planning. This algorithm is further implemented as a distributed logistics controller (DLC) to enable all UGVs to collaborate within a group, aiming to achieve optimal task scheduling and minimize the longest completion time for all tasks. The proposed navigation and guidance methods are validated on a developed semi-physical simulation platform, and experimental results demonstrate that the UWB-based localization system can accurately guide the UGVs in complex paths, and the DLC effectively reduces the logistics delivery time while maintaining stability and reliability.

Mathematical model for radar detecting of the ground-to air missile defense system is established by taking UAV coordinated formation-to-the ground penetration. Based on WSEIAC model, model for the effectiveness of multiple UAVs coordinated formation is founded from such three aspects as target discovery, radar cross-section and target killing. Researches with respect to influences towards penetration effectiveness by scale, density and pattern of the formation under typical penetration conditions have been conducted. Combining with concrete cases and simulation results, validity of the evaluation method for penetration effectiveness of multiple UAVs coordinated formation is verified.

Multiple UAVs collaborative operation which has high-level of autonomy will be an important mode of the future war. Fuzzy Virtual Force is proposed to solve the problems of collision avoidance and simultaneous arrival in collaborative UAVs path planning. Collision is avoided based on extended threats from which the repulsions are utilized to optimize the space of UAVs and to ensure their safety. The estimated time until arrival is used to regulate the flight speed by iteration to achieve the simultaneous arrival. Simulation results demonstrate that the proposed approach is feasible and useful to fulfill the purpose of collaboration of space and time.

为解决信息不完备条件下的无人作战飞机（UCAV,Unmanned Combat Air Vehicle）战术决策问题,提出一种基于灰色区间关联的UCAV自主战术决策方法.依照作战任务要求选取决策要素,建立UCAV决策推理的规则库.构建不完备信息模型,并基于灰色区间关联理论给出UCAV战术决策模型;设计冲突消解算法,有效解决不完备信息导致的推理失效问题.仿真实例模拟了决策过程,验证了该方法在解决UCAV战术决策问题上的可行性和在化解规则匹配冲突方面的有效性.仿真结果表明,该方法能够应对决策要素不确定性较大的情况,并给出合理的战术行为推理结果.