军事卫星通信系统课程是军校本科学员的首次任职专业课,对照教育部提出的"金课"建设标准,结合军队院校课程为战教战的要求,提出面向"四性一度"的军事卫星通信系统"金课"建设方案.围绕课程教学目标制定、课程教学内容优化、教学模式设计、课程资源建设与课程考核机制等方面进行具体阐述,该课程建设聚焦课程教学铸魂性和为战性军事人才培养要求,着重强调课程的高阶性、创新性、挑战度,可为面向特定岗位任职的专业课程建设提供借鉴.

结合军队院校卫星通信专业课程建设,针对互联网先进信息技术带来的教与学的新变革,立足教育部提出的"两性一度"金课建设标准,以军事院校立德树人、为战育人的人才培养导向,分析军队院校专业课教学中存在的"痛点",将传统线下课堂与线上学习的优势相结合,构建课前、课中和课后三个环节进阶式的线上线下混合式教学模式,并基于成果导向教育(Outcome based education,OBE)理念提出军队院校专业课程"金课"建设的具体措施,为军队院校专业课程线上线下教学模式创新提供了参考价值.

The unbalanced burden on the nodes nearing the ground station pose challenges on the multi-hop data transmission in aerial sensor networks(ASNs).In order to achieve reliable and efficient multi-hop data transmission in ASNs,a reinforcement-learning based queue-efficient geographic routing(RLQE-GR) protocol is proposed.The RLQE-GR protocol maps routing problem into the general reinforcement learning(RL) framework,where each UAV is treated as one state and each successful packet forwarding is treated as one action.Based on the framework,the RLQE-GR protocol designs a reward function related to geographical location,link quality and available transmission queue length.Then,the Q-function is employed to converge all the sta-teaction values(Q-values),and each packet is forwarded based on potential state-action values.To converge all Q values and minimize performance deterioration during the convergence process,a beacon mechanism is employed in RLQE-GR protocol.In contrast to existing geographic routing protocols,the RLQE-GR protocol simultaneously takes the queue utilization,link quality and relative distance into consideration for forwarding packets.This makes the RLQE-GR protocol achieve load balancing,meanwhile not introducing strict performance deteriorations on routing hop and link quality.Moreover,due to the near-optimization character of RL theory,the RLQE-GR protocol can achieve routing performance optimization on packet delivery ratio and end-to-end delay.

空中自组网(Flying Ad-Hoc Network,FANET)是支撑无人机集群系统(Unmanned Aerial Vehicle Swarm,UAV swarm)的关键技术,它由数量庞大且具有无线通信能力的小型无人机构成.FANET中的信标帧业务在实现集群一致性控制应用的过程中扮演着重要角色.然而,实际应用中FANET无线链路的不可靠性将会导致信标帧出现丢包现象,进而影响一致性控制算法的收敛速度(或收敛时间),即集群所有状态值趋于一致的快慢程度.从理论上分析一致性控制算法收敛性能与信标帧丢包率之间的解析关系,对一致性控制算法在未来FANET中的应用具有举足轻重的意义.针对上述研究背景,文中提出了一种基于随机有向图模型和矩阵论的收敛性能分析模型.该模型将每个周期内FANET中的信息流抽象为随机有向图,并采用指示矩阵来表示该随机有向图的拉普拉斯矩阵,有效地用矩阵多项式对一致性收敛过程进行建模.随后,基于矩阵运算和矩阵谱半径的相关知识,该模型给出了最终期望收敛值的解析表达式.利用该最终期望收敛值,所提模型定义了新的收敛速度量化方法.与现有收敛速度分析工作不同,文中通过评估所有节点的初始状态值收敛到期望收敛值的快慢来对收敛速度进行量化,而不是根据收敛到每个周期网络的平均状态值来进行量化.基于矩阵运算和矩阵谱半径相关知识,所提模型给出了该收敛速度与信标帧丢包率之间的耦合关系,并根据该耦合关系推导出了收敛时间的表达式.仿真结果表明,所提收敛性能分析模型能够准确地描述实际FANET中收敛速度随时间的变化情况.此外,该模型能够准确描述实际FANET中每条链路的平均丢包率、状态值初始分布以及无人机节点个数的变化趋势对收敛时间的影响.同时,相比现有收敛性能分析模型,所提模型得到的收敛性能曲线更接近实际FANET中的收敛性能曲线.

MIMO雷达采用ISAR技术成像时需要估计目标的运动参数,为回波数据的方位向重排与插值提供依据,以及实现距离-多普勒图像的横向定标.针对匀加速旋转目标,该文提出一种初始角速度和转动加速度联合估计方法.借助MIMO雷达多通道观测的结构优势,根据不同通道回波间相位差异,通过估计差异信号相位系数获得目标运动状态.在此基础上,分析了算法推导过程中因函数近似引起的误差,同时对算法的分辨能力给出定量评估.最后,通过仿真验证了分析推导的正确性.

Traditional monitoring software of communication devices have some shortcomings,for instance,Human-machine interaction(HMI) is not friend enough,software cannot monitor or control devices one-to-many,and the display is not visual or real-time enough.In order to resolve these problems,a design technique of a common monitoring and business communicating software of communication devices was proposed,whose HMI was more friend.And it could monitor and control devices one-to-many,also it had better real-time performance.

A sequential approach was used for disturbance cancellation in passive radar,which was based on projections of the received signals in a subspace orthogonal to disturbance.Then the sequential cancellation algorithm was simulated to process both the theoretical data and radar received data to prove its feasibility and validity.