基于误差四元数的单兵导航系统算法

MEMS传感器在尺寸、价格和功耗方面的进步促进了单兵导航系统的发展.对基于MEMS的单兵导航系统,航向估计是GPS信号丢失情况下的一个基础性难题,准确的航向估计能有效抑制导航误差的漂移.为获得准确的航向信息,减小单兵定位误差,提出了基于误差四元数的数据融合算法,利用卡尔曼滤波算法估计误差四元数,并进行了场地实验以验证算法的有效性.结论证明:当步行距离为80 m时,定位误差小于总距离的3％.     

一种基于条件证据网络的多源异类知识融合识别方法

针对融合识别领域中不同框架下多源异类传感器的不确定证据信息无法有效融合的问题, 提出一种基于条件证据网络的多源异类知识融合识别方法. 该方法将战场协同作战中不同框架下多源异类传感器的领域知识统一在证据网络的结构下, 形成多源异类知识融合识别模型, 对多源异类传感器的不确定性证据信息进行基于条件证据网络的融合推理, 得到识别结果. 仿真实例验证了所提出方法的优越性.

     

交会对接仿真电视图像TMO算法主观评价

通过对实时渲染仿真交会对接电视图像的主观评价,指出调和映射处理(TMO)算法的修正是提高仿真场景逼真度的方法.基于交会对接仿真电视图像TMO算法应用,分别针对S-curve算法、自适应对数算法和Reinhard算法设计了基于静态图像对比的亮度调和因子评价实验和基于动态场景对比的算法应用距离段评价实验,并对实验结果的均值、标准差以及HSD值进行了计算与分析;上述3种算法亮度调和因子分别为6.0,0.6和2.2,依次应用于远距离,中、近距离和靠拢距离时仿真电视图像有最佳评价和显示效果.实验结果表明,采用同种TMO算法处理的交会对接仿真图像动态场景真实感效果优于静态图像;在交会对接不同距离段内,仿真场景需要采用不同的TMO渲染方法.     

基于神经二部分裂结构的多智能体WSNs数据融合算法?

为进一步提高无线传感器网络WSNs( Wireless Sensor Networks)使用寿命,从提高算法数据融合效率角度出发,提出一种神经二部分裂结构的多智能体WSNs数据融合算法。首先,根据神经结构的主干与枝干承载信息量不同的原理,选取主干、枝干通讯链路并赋予较大能量,并给出主、辅中心节点选取方法;其次,设计了基于LMS的自适应加权融合算法,分别针对节点层级、枝干中心层级和主干中心层级进行逐层处理,实现了对神经二部分裂结构的数据融合;最后,通过与两种已有算法进行仿真对比,显示本文算法在Sink节点接收数据包,能耗等指标上均具有优势,验证了算法有效性。     

Fuzzy decision fusion of complementary experts based on evolutionary cepstral coefficients for phoneme recognition

Optimal representation of acoustic features is an ongoing challenge in automatic speech recognition research. As an initial step toward this purpose, optimization of filterbanks for the cepstral coefficient using evolutionary optimization methods is proposed in some approaches. However, the large number of optimization parameters required by a filterbank makes it difficult to guarantee that an individual optimized filterbank can provide the best representation for phoneme classification. Moreover, in many cases, a number of potential solutions are obtained. Each solution presents discrimination between specific groups of phonemes. In other words, each filterbank has its own particular advantage. Therefore, the aggregation of the discriminative information provided by filterbanks is demanding challenging task. In this study, the optimization of a number of complementary filterbanks is considered to provide a different representation of speech signals for phoneme classification using the hidden Markov model (HMM). Fuzzy information fusion is used to aggregate the decisions provided by HMMs. Fuzzy theory can effectively handle the uncertainties of classifiers trained with different representations of speech data. In this study, the output of the HMM classifiers of each expert is fused using a fuzzy decision fusion scheme. The decision fusion employed a global and local confidence measurement to formulate the reliability of each classifier based on both the global and local context when making overall decisions. Experiments were conducted based on clean and noisy phonetic samples. The proposed method outperformed conventional Mel frequency cepstral coefficients under both conditions in terms of overall phoneme classification accuracy. The fuzzy fusion scheme was shown to be capable of the aggregation of complementary information provided by each filterbank.     

Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

The information fusion estimation problems are investigated for multi-sensor stochastic uncertain systems with correlated noises. The stochastic uncertainties caused by correlated multiplicative noises exist in the state and observation matrices. The process noise and the observation noises are one-step auto-correlated and two-step cross-correlated, respectively. While the observation noises of different sensors are one-step cross-correlated. The optimal centralized fusion filter, predictor and smoother are proposed in the linear minimum variance sense via an innovative analysis approach. To enhance the robustness and flexibility, a distributed fusion filter is put forward, which requires the calculation of filtering error cross-covariance matrices between any two local filters. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection (CI) fusion algorithm, which can reduce the computational cost. A simulation example is given to show the effectiveness of the proposed algorithms.     

视景建模中树木纹理图像的随机变形网格方法

目的 视景建模中,采用“公告板”贴图法对同树种不同个体树建模时,需生成各种形态各异的树木纹理图像,为此提出一种新的生成树木纹理图像的随机变形网格方法。方法 首先将树木的原始纹理图像进行网格划分,再对网格顶点进行随机变形,最后将生成的不规则网格中的图像映射到新图像中相应的规则网格上,得到变形后的纹理图像。结果 应用随机参数控制树木图像的变形,模拟出成长环境因素对树木形态的影响。本文方法比采集多幅个体树图像贴图方法更简便,容易实现;比用同一幅图像进行多个个体贴图的视觉效果更真实。结论 本文方法生成的树木纹理图像应用于“公告板”贴图时,在视景模型中能较好地模拟出同一树种的多个个体树情景。该方法基于图像的局部变形,仍然保持原始图像内容的整体基本形态和内部结构的连续性,因此,同样适用于其他不规则形状的自然景物(如山峦、河流、海浪等)的图像处理。     

SAP与物采系统融合衔接流程设计

本文主要介绍了物采系统,重点分析了SAP与物采系统融合衔接中的问题,针对这些问题提出了相应的融合衔接设计了,解决了SAP与物采系统数据传递,从而形成了物资采购从谈价议价、招标、反向拍卖到产生采购订单付款一个完整科学管理信息流程,满足了物资采购管理和采购交易为一体的业务管理需求,为以后其他系统与SAP系统融合提供了借鉴意义。     

SAP与FMIS融合系统的设计应用研究

文章主要通过SAP与FMIS系统融合问题,重点分析介绍了融合系统的设计方案和实施,解决了SAP与FMIS系统数据传递、财务数据差异等问题。为以后其他系统与SAP系统融合提供了重要借鉴意义。     

Mutual spectral residual approach for multifocus image fusion

Multifocus image fusion is the process of obtaining a single image from multiple partially focused images such that the newly formed image consists of the well-defined information extracted from each source image. This paper proposes the use of saliency of the source images based on Mutual Spectral Residual. Spectral Residual brings out the unique/salient features of an image in frequency domain. The idea of proposed mutual spectral residual is to emphasize the relative unique features of a source image with respect to the other source images. The relative unique features are utilized to form saliency maps for each source image. These saliency maps can clearly indicate the focused and defocused parts of an image. Based on the saliency maps obtained, the image fusion takes place in spatial domain. Visual inspection and quantitative evaluation of the fused images obtained by the proposed method, using different evaluation metrics, demonstrate its effectiveness over several existing image fusion methods.