地磁辅助惯性组合导航技术分析

从地磁导航的特点出发,指出了地磁／惯性组合导航在未来发展中的优势,并对实现该种组合导航需要的技术手段做了全面的分析,包括：信息融合策略、高精度地磁场模型的构建方式以及滤波算法的设计。同时,阐述了其中的改进方向,为进一步提高该种组合导航性能提供思路。     

规划专题影像地图的概念重构与视觉融合设计

针对空间规划领域中专题影像地图的制图应用,对影像地图的分类体系、规划专题影像的制图框架及其中影像的功能角色等进行概念重构,并提出以"矢量与栅格、基础与专题"等关系的处理为核心,以色彩配置、符号构形、构图整饰为主体,以视觉变量协调为关键的视觉融合设计策略;示例证明能够有效指导对规划方案概念的系统表达。     

基于专家评价信息的军校课堂教学质量评价的统计融合方法

以现代军事教育教学理论为指导,结合军校课堂教学质量评价指标体系和军校课堂教学评价的实际,提出了一种基于专家评价信息的军校课堂教学质量评价的统计融合方法。通过实例分析和计算,验证了该方法的有效性。     

基于小波变换的自适应梯度边缘检测算法

针对传统的单一边缘检测算法抗噪能力差、边缘不连续等不足,本文提出采用两种算法相结合的方式来进行边缘检测。首先,对原始图像进行多层小波分解;然后,对小波分解后的图像低频部分用提出的8点邻域自适应梯度算法进行边缘检测,依靠边缘生长方法保证检测出的边缘的连续性,对高频部分用小波变换的局部模极大值算法检测图像的边缘;最后,将各层边缘信息按一定的融合规则融合起来得到最终的图像边缘。实验结果表明,该方法与传统的边缘检测算法相比具有定位精度高、去噪效果好等明显的优点,也能较准确地提取图像的边缘。     

基于粒子滤波的TOA/TDOA融合算法研究

针对信号临界区域由于信号强度减弱导致的定位精度下降问题,利用一种传感器检测概率模型把收集到所有的TOA与TDOA量测信息通过似然度计算融合起来,在贝叶斯框架下建立一种非线性目标基于粒子滤波器的量测信息融合算法对目标进行定位估计,从而提高目标在临界区域的定位精度。仿真结果表明,在低信噪比下,融合两种观测手段的定位精度改善明显。     

基于加权数据融合和相关性的微型电子测压器改进的动态校准方法

为确保微型电子测压器在高温、高压、高冲击的实测环境中的测试精度,缩短校准周期,设计了模拟火炮膛内压力环境下的微型电子测压器动态校准系统,并从"虚拟"标准测试系统的建立和测压器动态灵敏度的计算两方面介绍了原始和改进的动态校准方法.对比分析其结果可知,改进的动态校准方法不仅能提高微型电子测压器的测试精度,而且能有效缩短校准周期,从而验证了改进的动态校准方法的可行性和合理性.     

无线传感器网络测试系统数据融合研究

根据无线传感器网络在军事领域中研究与应用,对无线传感器网络用于军事装备测试成为新的应用方向.针对无线传感器网络在军事装备测试系统中数据冗余大、关联性强、实时性要求高等问题,为提高传输率与数据精度,提出了基于核偏最小二乘法的无线传感器网络数据融合算法.利用核偏最小二乘法对数据进行回归建模并进行仿真,实现数据快速融合.仿真结果表明,方法的可行性和有效性.     

未知环境下的移动机器人环境建模研究

环境建模技术是移动机器人自主导航研究中的一个关键问题。本文给出一种基于多传感器信息融合的环境建模方法。实验结果表明该方法有效地克服了传感器的累计误差,有效地提高了环境建模的准确性。此方法的可行性和有效性通过Pioneer3-DX移动机器人得到了实验验证。     

Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing

Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m^− 1) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted (R² = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models.     

Detailed Real-Time Urban 3D Reconstruction from Video

The paper presents a system for automatic, geo-registered, real-time 3D reconstruction from video of urban scenes. The system collects video streams, as well as GPS and inertia measurements in order to place the reconstructed models in geo-registered coordinates. It is designed using current state of the art real-time modules for all processing steps. It employs commodity graphics hardware and standard CPU’s to achieve real-time performance. We present the main considerations in designing the system and the steps of the processing pipeline. Our system extends existing algorithms to meet the robustness and variability necessary to operate out of the lab. To account for the large dynamic range of outdoor videos the processing pipeline estimates global camera gain changes in the feature tracking stage and efficiently compensates for these in stereo estimation without impacting the real-time performance. The required accuracy for many applications is achieved with a two-step stereo reconstruction process exploiting the redundancy across frames. We show results on real video sequences comprising hundreds of thousands of frames.