ID 原文 译文
38566 设计了基于多跳位置估计的无线光移动自组织网络拓扑重构方法,该方法不依赖定位系统,如全球定位系统(global positioning system,GPS)等, Design based on the multiple hops position estimation method of wireless mobile self-organized network topology reconstruction of light, the method does not depend on the positioning system, such as global positioning system (global positioning system, GPS), etc. ,
38567 也不需要无线电通信辅助,仅采用自由空间光(free space optical,FSO)对网络中其他节点进行方向和距离估计,位置估计信息通过多跳方式传递,用于建立重构链路,增加节点连通度,提高网络性能。 also do not need radio communication support, adoption of free space optical (free space optical, FSO) to other nodes in the network is used to estimate the direction and distance, position estimation messages through multiple hops way, used for creating refactoring link, increase the node connectivity, improve the network performance.
38568 该方法分析了多跳节点间的位置不确定区域,并提出了覆盖不确定区域的光波束分配算法用于新的FSO链路建立。 The method analyzes the multiple hops node location of the uncertain area, and puts forward the beams of light cover uncertain area allocation algorithm for new FSO link establishment.
38569 仿真表明,在节点规模小于20的自组织网络中,光束发散角大小与距离估计误差决定相对定位精度,并影响重构网络节点端到端性能,通过减小发散角并提高光检测灵敏度,该方法的性能接近基于GPS定位的重构方法。 Simulation shows that, in the node size less than 20 self-organizing network, the size of the beam divergence Angle and distance estimation error decision relative positioning accuracy, and influence the reliability of reconstructing end-to-end network node, by reducing the divergence Angle and high detection sensitivity, the method of performance close to the reconstruction method based on GPS.
38570 精确导航技术是高超飞行器(hypersonic vehicle,HV)充分发挥威力的关键所在。 Precision navigation technology is superb aircraft (hypersonic vehicle, HV) give full play to the key to power.
38571 然而,高马赫数和强机动性致使HV的导航系统误差及其观测噪声无法准确描述,制约着导航信息的精确性和实时性。 High Mach number and strong maneuverability, however, the HV navigation system error and the observation noise can't accurately describe, limits the accuracy and real-time performance of navigation information.
38572 为及时获取高精度导航信息,设计基于集员框架的卡尔曼滤波算法。 To get high precision navigation information in a timely manner, design the kalman filtering algorithm based on frame of set membership.
38573 一方面采用多智能体分布式协同探测,形成观测椭球交叉集合,提高了观测效率和测量精度; On the one hand, using multi-agent distributed collaborative detection, form observation ellipsoid cross collection, improve the efficiency of the observation and measurement accuracy;
38574 另一方面,通过设计两类噪声模型,求其与状态估值的最小均方误差,实现滤波增益的计算,提高算法对噪声的抗扰动能力,使状态估值达到均方误差最小。 On the other hand, through the design of two types of noise model, and with the minimum mean square error of the state, implement the filter gain calculation, improve the algorithm of noise disturbance resistance, as to achieve the minimum mean square error (mse) of the state.
38575 通过数字仿真,将设计方法应用到HV导航模型中,并与扩展卡尔曼滤波和集员滤波的状态估值进行比较,结果表明,提出算法在不同噪声影响下具有更高的估计精度。 Through digital simulation, the design method is applied to HV navigation model, and with the extended kalman filtering and comparing the state set membership filter, the results show that the proposed algorithm has higher estimation precision under different noise effects.