无线传感器网络中多传感器动态自适应调度算法

无线传感器网络中,目标跟踪往往通过节点之间的协作完成。在无迹变换卡尔曼滤波基础上,提出一种多传感器动态自适应调度算法进行目标跟踪。该方法根据预测跟踪精度来确定采样间隔,然后基于特定的检测概率,为下一时刻选择一组传感器,形成一个临时工作组,并指定某一个传感器作为中心节点进行数据融合。仿真结果证明了该算法能有效提高跟踪精度和可靠性。     

含万向铰偏斜轴系的过渡过程横扭耦合振动特性

为提供船艇推进、火炮、航空动力及传动装置机械设计的理论分析基础,建立了加速度冲击下含万向铰偏斜轴系横扭耦合振动模型,揭示了轴系的过渡过程横扭耦合振动特性。将从动轴视为弯曲刚性、扭转柔性的轴,用投影平面上动态偏斜角表示轴系横向振动,用拉格朗日方程推导了万向铰轴系过渡过程横扭耦合三自由度振动微分方程,结合振动响应仿真给出了加速度和系统参数对轴系过渡过程横扭耦合振动特性的影响规律。结果表明:负载转矩增大和刚度不对称性减小导致耦合振动系统颤振失稳区增大;在超谐共振区内,加速度增大对过渡过程系统振动起稳定作用,转矩、刚度和偏斜角增大导致过渡过程系统振动失稳区域变大。     

一种无人地面车的轨迹跟踪控制方法

针对无人地面车辆经典轨迹跟踪控制方法的不足,提出了一种基于无人地面车辆运动学模型的轨迹跟踪控制算法。在算法中引入了位姿的纵坐标误差,以加快跟踪的逼近速度,并利用人工驾驶的思想,采用了一种位一姿交替控制的方法,使路径跟踪既准确又快速。仿真结果表明：该方法是有效的,且可使无人地面车具有更好的跟踪性能。     

一种可移动式科氏流量计实验平台设计与实现

为提高科氏流量计的计量精度,促进相关数字信号处理方法与技术的发展和进口高精度科氏流量计的国产化,设计了一种可移动式科氏流量计实验平台。简述了科氏流量计的基本原理和相关数字信号的处理方法,指明了相关方法的研究趋势。针对研究趋势,分析了科氏流量计实验平台的功能需求,给出了总体设计方案、硬件选型、软件流程和技术指标,重点阐述了数据采集模块(以PLC和NI9234为核心部件)的详细设计,给出了实验平台的初步测试结果。测试表明,该实验平台性能稳定,功能全面,能较好地为开展科氏流量计相关数字信号处理方法研究提供平台支撑。     

基于多传感器径向速度测量的目标跟踪方法

针对基于位置信息的目标跟踪在初始跟踪和运动模型变化阶段航向估计精度低的情况,利用传感器径向速度测量精度较高的特性,提出了一种基于多传感器径向速度测量的目标跟踪方法,解决数据更新周期长、定位精度低情况下目标跟踪的精度问题。仿真实验证明采用文中所提方法对跟踪精度有较大的提高。     

一种舰炮武器系统手动跟踪及射击控制方法

针对常规舰炮武器系统对低照度、低反射面积目标无法自动跟踪和有效测距的情况,设计了一种基于光电传感器目标视频的手动跟踪及射击控制方法。操作手控制跟踪器对目标实时瞄准,并通过激光测距或距离装定,以获取目标的量测数据。该方法原理简单、成本低、易于实现,并且具有诸元快速校正功能,已成功应用于工程实践,并经过试验验证,可以有效提高系统对该类目标的作战能力。     

Optimal joint preventive maintenance and production policies

We study joint preventive maintenance (PM) and production policies for an unreliable production‐inventory system in which maintenance/repair times are non‐negligible and stochastic. A joint policy decides (a) whether or not to perform PM and (b) if PM is not performed, then how much to produce. We consider a discrete‐time system, formulating the problem as a Markov decision process (MDP) model. The focus of the work is on the structural properties of optimal joint policies, given the system state comprised of the system's age and the inventory level. Although our analysis indicates that the structure of optimal joint policies is very complex in general, we are able to characterize several properties regarding PM and production, including optimal production/maintenance actions under backlogging and high inventory levels, and conditions under which the PM portion of the joint policy has a control‐limit structure. In further special cases, such as when PM set‐up costs are negligible compared to PM times, we are able to establish some additional structural properties. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.     

极化域—空域联合谱估计

首次提出极化域-空域联合谱的概念,拓广了电磁波特征参量的谱描述方法.结合具体的极化敏感阵列定义了极化域-空域联合谱,用MUSIC方法得到联合谱函数表达式.研究了联合谱的估计的重要性能指标估计精度和分辨力,估计精度用克拉美劳限CRB(cramer-Rao Bound)来具体度量,分辨力由极化域-空域模糊函数以及模糊图来描述.最后仿真结果验证了该方法的有效性和理论分析的正确性.     

基于模态分析的挠性接头角刚度测量研究

挠性接头是动力调谐陀螺的关键元件,刚度是其重要指标。针对挠性接头角刚度的测量,提出了一种基于模态分析的动态角刚度测量方法。该方法采用阶跃激励,利用高精度传感器和数据采集卡测量挠性接头的振动并采集数据,然后对采集数据进行数字滤波、FFT变换和频谱细化,计算出挠性接头的动态角刚度,同时相应地求出了角阻尼比系数。该方法解决了长期以来采用静态测试法测量挠性接头角刚度所带来的问题,具有测量精度高、操作方便、测试效率高、重复性好等特点。     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.