首届中国无人机低空遥感测绘创新论坛圆满结束

4月6日，首届中国无人机低空遥感测绘创新论坛在北京会议中心成功召开。本届论坛由中国无人机任务系统及技术产业联盟、中航出版传媒有限责任公司、广东泰一高新技术发展有限公司共同主办，《无人机》杂志协办。论坛以“无人机与数字中国”为主题，就国家基础测绘、应急救援测绘、数字城市和智能交通等热点应用领域进行了深入探讨和交流，成果丰硕。

俄将在雅克-130上研制无人机

【据《无人机》2015年9月18日报道】俄罗斯无线电领域中的最大控股公司——无线电电子技术集团第一副总经理顾问弗拉基米尔·米赫耶夫向俄新社表示，在推重比与多用性方面超过美国MQ-9“收割者”的无人攻击机或将在雅克-130教练战斗机的基础上研制。

无人机发展论坛在京召开

2016年10月24日，无人机发展论坛在北京亦创国际会展中心举办。本次论坛由北京市人民政府、工业和信息化部、中国科学技术协会指导，中国电子学会、中航出版传媒有限责任公司主办，《无人机》杂志承办，中国航空学会无人驾驶航空器系统专门委员会与深圳市无人机行业协会支持。来自政府、工业界、学术界及院校从事无人机相关领域工作的权威领导、专家、学者等近百人出席了论坛。

高科技下的人性抉择《无人机》

上映于2013年的惊悚大片《无人机》虽没有炫丽的科技特效，但导演却巧妙地将视角移到了战争中的幕后英雄—无人机驾驶员，并通过一次驾驶无人机完成的任务，将对人性的拷问展现在观众面前。

首届中国无人机低空遥感测绘创新论坛成功召开

4月6日，首届中国无人机低空遥感测绘创新论坛在北京会议中心成功召开。本届论坛由中国无人机任务系统及技术产业联盟，中航出版传媒有限责任公司、广东泰一高新技术发展有限公司主办，《无人机》杂志协办。论坛以“无人机与数字中国”为主题，来自全国各地无人机界与遥感测绘专业领域的领导、专家，学者和企业家等200余人出席会议。

2019中国无人机系统峰会成功举办

2019年8月30日,2019中国无人机系统峰会落下帷幕。本届峰会由中航出版传媒有限责任公司、海鹰航空通用装备有限责任公司、北京无人机与航空应用服务产业技术创新联盟、中国宇航学会无人机专业委员会主办,《无人机》杂志及北航校友通航协会无人机专委会承办。峰会嘉宾立足无人机创新发展和未来应用,用专业化的视野与来宾展开了广泛深入的研讨,提出了很多前瞻性的观点和建议,来自军方、政府部门、各大军工企业、产业界、学术界的近200名参会代表出席会议。

Research on fast detection method of infrared small targets under resourceconstrained conditions

Infrared small target detection is a common task in infrared image processing.Under limited computa⁃tional resources.Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy.A fast infrared small target detection method tailored for resource-constrained conditions is pro⁃posed for the YOLOv5s model.This method introduces an additional small target detection head and replaces the original Intersection over Union(IoU)metric with Normalized Wasserstein Distance(NWD),while considering both the detection accuracy and the detection speed of infrared small targets.Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU,while reach⁃ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.

Improved YOLOv8-Based Target Detection Algorithm for UAV Aerial Image

In response to the challenge of low detection accuracy and susceptibility to missed and false detections of small targets in unmanned aerial vehicles(UAVs)aerial images,an improved UAV image target detection algorithm based on YOLOv8 was proposed in this study.To begin with,the CoordAtt attention mechanism was employed to enhance the feature extraction capability of the backbone network,thereby reducing interference from backgrounds.Additionally,the BiFPN feature fusion network with an added small object detection layer was used to enhance the model's ability to perceive for small objects.Furthermore,a multi-level fusion module was designed and proposed to effectively integrate shallow and deep information.The use of an enhanced MPDIoU loss function further improved detection performance.The experimental results based on the publicly available VisDrone2019 dataset showed that the improved model outperformed the YOLOv8 baseline model,mAP@0.5 improved by 20%,and the improved method improved the detection accuracy of the model for small targets.

Monitoring coal fires in Datong coalfield using multi-source remote sensing data

Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.

AC-PSO ALGORITHM FOR UAV MISSION PLANNING

Choosing the best path during unmanned air vehicle （UAV） flying is the target of the UAV mission planning problem. Because of its nearly constant flight height, the UAV mission planning problem can be treated as a 2-D （horizontal） path arrangement problem. By modeling the antiaircraft threat, the UAV mission planning can be mapped to the traveling seaman problem （TSP）. A new algorithm is presented to solve the TSP. The algorithm combines the traditional ant colony system （ACS） with particle swarm optimization （PSO）, thus being called the AC-PSO algorithm. It uses one by one tour building strategy like ACS to determine that the target point can be chosen like PSO. Experiments show that AC-PSO synthesizes both ACS and PSO and obtains excellent solution of the UAV mission planning with a higher accuracy.

REAL-TIME TRACKING FOR FAST MOVING OBJECT ON COMPLEX BACKGROUND

A real-time tracking system for the fast moving object on the complex background is proposed.The Markov random filed（MRF）model based background subtraction algorithm is used to detect the changing pixels and track the moving object.The prior probability of the segmentation mask is modeled by using MRF,and the object tracking task is translated into the maximum a-posterior（MAP）problem.Experimental results show that the method is efficient at both offline and online moving objects on simple and complex background.

GUIDANCE AND CONTROL FOR AUTOMATIC LANDING OF UAV

A scheme of guidance and control is presented to meet the requirements for automatic landing of unmanned aerial vehicles (UAVs) based on the airborne digital flight control system and radio tracker on ground station. An automatic landing system is realized for an unmanned aerial vehicle. The results of real time simulation and flight test are given to illustrate the effectiveness and availability of the scheme. Results meet all the requirements for automatic landing of the unmanned aerial vehicle.

INTEGRATED DESIGN OF AN ELECTRIC POWERED UNMANNED AIR VEHICLE USING CONCURRENT SUBSPACE DESIGN

The concurrent subspace design (CSD) framework has been used to conduct a preliminary design optimization of an electric powered, unmanned air vehicle (EPUAV) operating at a low Reynolds number. A multidisciplinary system analysis that includes aerodynamics, weights, propulsion, performance and stability and control has been developed for this class of vehicles. The CSD framework employs artificial neural network based response surfaces to provide approximations to the design space. The EPUAV system includes 25 continuous and 4 discrete design variables. The CSD framework was able to identify feasible designs with significant weight reductions relative to any previously considered (i.e. initial database) designs. This was accomplished with a limited number of system analyses. The results also demonstrate the nature of this design framework adaptive to changes in design requirements.

IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR INTELLIGENTLY SETTING UAV ATTITUDE CONTROLLER PARAMETERS

An improved particle swarm optimization （PSO） algorithm is investigated in the optimization of the attitude controller parameters of unmanned aerial vehicle （UAV）. Considering the stagnation phenomenon in the later phase of the basic PSO algorithm caused by the diversity scarcity of particles, a modified PSO algorithm is presented. For the basic PSO algorithm, the velocity of each particle is adjusted according to the inertia motion, the swarm previous best position and its own previous best position. However, in the improved PSO algorithm, each particle only learns from another randomly selected particle with higher performance, besides keeping the inertia motion. The inertia weight of the improved PSO algorithm is a random number. The modification decreases the uncertain parameters of the algorithm, simplifies the learning mechanism of the particle, and enhances the diversity of the swarm. Furthermore, a UAV attitude control system is built, and the improved PSO algorithm is applied in the optimized tuning of four controller parameters. Simulation results show that the improved PSO algorithm has stronger global searching ability than the common PSO algorithms, and obtains better UAV attitude control parameters.

Effects of Supplementary Pollination by Single-rotor Agricultural Unmanned Aerial Vehicle in Hybrid Rice Seed Production

In 2012-2015, under the conditions of different natural wind speeds, the single-rotor agricultural unmanned aerial vehicle was used for the supplementary pollination during seed production of 10 hybrid combinations with big parental row ratios in the hybrid rice seed production bases of Hunan, Hainan and Guangdong Province, and the pollination effects were studied through the investigation of pollen density in the field, outcrossing seeding rate of female parent and seed production yield. The results showed that under the parental row ratio of 6：（40-60）, the seed setting rate and yields of the supplementary pollination by single-rotor agricultural UAV could reach and even higher than those of artificial pollination, indicating the single-rotor agricultural UAV could be used in supplementary pollination for hybdd rice seed production, which could promote the whole-process mechanization of seed production.

ADAPTIVE NEURAL NETWORK ATTITUDE CONTROL FOR UNMANNED HELICOPTER

Adaptive flight control technology, feedback linearization, model inversion theory are reviewed and the error dynamic characteristics are analyzed, and an adaptive on-line neural network attitude control system is presented. The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals. Simulation results demonstrate that the nonlinear neural network augmented model inversion can self-adapt to the uncertainty and modeling errors of unmanned helicopters. Results are compared while the parameters of PD controller and robustness items are changed.

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.

An adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs

To overcome nonlinear and 6-DOF(degrees of freedom)under-actuated problems for the attitude and position of quadrotor UAVs,an adaptive backstepping sliding mode method for flight attitude of quadrotor UAVs is proposed,in which an adaptive law is designed to online estimate the parameter variations and the upper bound of external disturbances and the assessments is utilized to compensate the backstepping sliding mode control.In addition,the tracking error of the design method is shown to asymptotically converge to zero by using Lyapunov theory.Finally,based on the numerical simulation of quadrotor UAVs using the setting parameters,the results show that the proposed control approach can stabilize the attitude and has hover flight capabilities under the parameter perturbations and external disturbances.

Cultivated land information extraction in UAV imagery based on deep convolutional neural network and transfer learning

The development of precision agriculture demands high accuracy and efficiency of cultivated land information extraction. As a new means of monitoring the ground in recent years, unmanned aerial vehicle （UAV） low-height remote sensing technique, which is flexible, efficient with low cost and with high resolution, is widely applied to investing various resources. Based on this, a novel extraction method for cultivated land information based on Deep Convolutional Neural Network and Transfer Learning （DTCLE） was proposed. First, linear features （roads and ridges etc.） were excluded based on Deep Convolutional Neural Network （DCNN）. Next, feature extraction method learned from DCNN was used to cultivated land information extraction by introducing transfer learning mechanism. Last, cultivated land information extraction results were completed by the DTCLE and eCognifion for cultivated land information extraction （ECLE）. The location of the Pengzhou County and Guanghan County, Sichuan Province were selected for the experimental purpose. The experimental results showed that the overall precision for the experimental image 1, 2 and 3 （of extracting cultivated land） with the DTCLE method was 91.7%, 88.1% and 88.2% respectively, and the overall precision of ECLE is 9o.7%, 90.5% and 87.0%, respectively. Accuracy of DTCLE was equivalent to that of ECLE, and also outperformed ECLE in terms of integrity and continuity.

Key Technology in Multi UAV Conflict Detection and Resolution Strategy

Based on ADS-B surveillance data,this paper proposes a multi-unmanned aerial vehicle(multi-UAV)collision detection method based on linear extrapolation for ground-based UAV collision detection and resolution,thus to provide early warning of possible conflicts.To address the problem of multi-UAV conflict,the basic ant colony algorithm is introduced.The conflict simplification model of the traditional basic ant colony algorithm is optimized by adding a speed regulation strategy.A multi-UAV conflict resolution scheme is presented based on speed regulation and heading strategies.The ant colony algorithm is improved by adding angle information and a queuing system.The results show that the improved ant colony algorithm can provide multi-UAV joint escape routes for a multi-UAV conflict situation in airspace.Unlike the traditional ant colony algorithm,our approach converges to the optimization target.The time required for the calculation is reduced by 43.9%,and the total delay distance caused by conflict resolution is reduced by 58.4%.