SMP2标准的原始数据生成模型框架分析

原始数据生成(RDG)是导航系统设计验证的重要组成部分。由其生成的数据可进行导航系统链路预算和误差预算,也可用来对完好性处理工具(IPF)和轨道与同步处理工具(OS-PF)的算法进行调整和确认。然而原始数据生成涉及到如天文地理、物理学等多学科领域的模型,不同模型之间的接口关系十分复杂,使得将这些模型集成并实现仿真验证功能变得十分困难,也难以有效实现仿真模型的可重用性和可移植性。基于SMP2标准的仿真系统设计开发方法能有效地解决上述问题,文章介绍了原始数据生成的概念,并在面向对象分析的基础上,提出了基于SMP2规范的模型集成开发的方法和步骤。     

单站多普勒跟踪定轨的优化算法

由于低轨道小卫星应用的日益广泛，在轨卫星数目的迅速膨胀，对传统的多个地面站相互协调管理卫星领域模式提出了挑战。小卫星的低成本特性必然要求存在与之相应的地面站设施降低复杂性、降低成本。本文针对当前已提出的单站多普勒定轨方案，进一步研究了多普勒定轨的有关方法，提出和建立了多普勒定轨的优化算法与模型，最后进行了仿真分析与模型的有效性验证。     

运载火箭姿控系统数学仿真软件设计

分析了运载火箭姿控系统箭体动力学运动方程和控制器数学模型特点，并且按照校正网络、执行机构、箭体方程、测量方程顺序设计数学仿真软件。该软件能方便地完成姿控系统数学仿真任务。由于采用三级模块结构设计，该软件具有结构清晰、层次分明、调试简单、功能齐全、使用方便等明显优点。     

可延伸出口锥对推力向量控制系统稳定性的影响

介绍了喷管可延伸出口锥对大型固体火箭发动机推力向量控制系统稳定性的影响,并提出了改善其系统稳定性的措施。     

卡尔曼滤波中动态模型的分级解耦

本文对卡尔曼滤波中动态模型的解耦提一种新的方法，这种方法其实质是把对整个动态模型的并联滤波估计变成对整个动态模型的分级串联滤波估计。这种分解耦的方法即可用于线性动态模型又可用于非线性动态模型。对于阶数很高的非线性动态模型，这种方法对于提高估计精度，减少量和计算时间，更有其优越性。本文利用一个九阶非线性动态模型进行仿真计算。计算结果说明了这种方法的有效性。     

测量数据的单位权化和单位权标准差

针对[4]中讨论的主要问题"测量值残差的单位权化”提出了不同的看法,并给出测量所产生的随机误差不同于测量值残差的理论依据和分析方法.     

高效图像资源管理器的设计与实现

针对数据存储量大引起的访问广域网服务器速度慢、不能对访问的客户进行细粒度的权限控制问题,研究了B/S模式下基于.NET平台的图像资源管理器的设计和实现技术。系统综合采用权限管理模型、动态加载、文档对象模型（DOM）等技术以及非点击无刷新伸缩控制方法,实现了图像资源管理器的分权限控制、高速访问以及无刷新高效联动等操作。实验验证了方法的可行性。     

Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas

Urban land cover information extraction is a hot topic within urban studies. Heterogeneous spectra of high resolution imagery—caused by the inner complexity of urban areas—make it difficult. In this paper a hierarchical object oriented classification method over an urban area is presented. Combining QuickBird imagery and light detection and ranging (LIDAR) data, nine kinds of land cover objects were extracted. The Spectral Shape Index (SSI) method is used to distinguish water and shadow from black body mask, with 100% classification accuracy for water and 95.56% for shadow. Vegetation was extracted by using a Normalized Difference Vegetation Index (NDVI) image at first, and then a more accurate classification result of shrub and grassland is obtained by integrating the height information from LIDAR data. The classification accuracy of shrub was improved from 85.25% to 92.09% and from 82.86% to 97.06% for grassland. More granularity of this classification can be obtained by using this method. High buildings and low buildings can, for example, be distinguished from the original building class. Road class can also be further classified into roads and crossroads. The comparison of the classification accuracy between this method and the traditional pixel-based method indicates that the total accuracy is improved from 69.12% to 89.40%.     

Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control (MPC) approach for coordination control of multiple Unmanned Aerial Vehicles (UAVs). First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization (CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem (FHOCP). Then, the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint. Further, an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach, which considers the predicted state errors and the convergence of cost function. Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.     

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs

The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.