Location Related Signals with Satellite Image Fusion Method Using Visual Image Integration Method

Investigations were performed on a group utilizing (General Purpose Unit) GPU and executions were evaluated for the utilization of the created parallel usages to process satellite pictures from satellite Landsat7.The usage on a realistic group gives execution change from 2 to 18 times. The nature of the considered techniques was assessed by relative dimensionless global error in synthesis (ERGAS) and Quality Without Reference (QNR) measurements. The outcomes demonstrate execution picks ups and holding of value with the bunch of GPU contrasted with the outcomes and different analysts for a CPU and single GPU. The errand of upgrading the view of a scene by combining data caught from various picture sensors is usually known as multisensor picture combination. This paper displays a territory based picture combination calculation to consolidate SAR (Synthetic Aperture Radar) and optical pictures. The co-enlistment of the two images is first led utilizing the proposed enrollment method prior to picture combination. The paper displays a parallel execution of existing picture combination techniques on a graphical group. Parallel executions of techniques in view of discrete wavelet changes are created. Division into dynamic and motionless regions is then executed on the SAR surface picture for particular injection of the SAR picture into panchromatic (PAN) picture. An integrated image in view of these two pictures is produced by the novel region based combination plot, which forces diverse combination rules for each fragmented region. At long last, this picture is melded into a multispectral(MS) picture through the half breed skillet honing technique proposed in past research. Exploratory outcomes exhibit that the proposed strategy demonstrates preferred execution over different fusion algorithms and can possibly be connected to the multisensory combination of SAR and optical pictures.     

基于约束最小二乘的纯方位多站被动跟踪算法

纯方位单站目标被动跟踪需要观测站作机动才能满足可观测条件,导致跟踪算法收敛时间较长。基于纯方位多站被动跟踪算法可以解决算法收敛时间较长的问题,文中提出了一种约束最小二乘算法用于纯方位多站被动跟踪,它首先为最小二乘算法引入约束条件,利用矩阵最小特征值所对应的特征向量,解决了EKF 算法需要初值的问题,避免了滤波的发散,同时也极大地减小了最小二乘估计的偏置。仿真表明,这种算法能够渐进地逼近估计误差的下限,并且其精度有所增加,是一种渐进、稳定以及近似无偏的估计算法。     

Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

For the multisensor linear discrete time‐invariant stochastic systems with unknown noise variances, using the correlation method, the information fusion noise variance estimators with consistency are given by taking the average of the local noise variance estimators. Substituting them into two optimal weighted measurement fusion steady‐state Kalman filters, two new self‐tuning weighted measurement fusion Kalman filters with a self‐tuning Riccati equation are presented. By the dynamic variance error system analysis (DVESA) method, it is rigorously proved that the self‐tuning Riccati equation converges to the steady‐state optimal Riccati equation. Further, by the dynamic error system analysis (DESA) method, it is proved that the steady‐state optimal and self‐tuning Kalman fusers converge to the global optimal centralized Kalman fuser, so that they have the asymptotic global optimality. Compared with the centralized Kalman fuser, they can significantly reduce the computational burden. A simulation example for the target tracking systems shows their effectiveness. Copyright © 2010 John Wiley & Sons, Ltd.     

Optimal and self‐tuning fusion Kalman filters for discrete‐time stochastic singular systems

Based on the optimal fusion estimation algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion Kalman filter weighted by scalars is presented for discrete‐time stochastic singular systems with multiple sensors and correlated noises. A cross‐covariance matrix of filtering errors between any two sensors is derived. When the noise statistical information is unknown, a distributed identification approach is presented based on correlation functions and the weighted average method. Further, a distributed self‐tuning fusion filter is given, which includes two stage fusions where the first‐stage fusion is used to identify the noise covariance and the second‐stage fusion is used to obtain the fusion state filter. A simulation verifies the effectiveness of the proposed algorithm. Copyright © 2008 John Wiley & Sons, Ltd.     

一种基于费歇信息增量的多传感器对多目标的分配方法

在对序贯Kalman滤波在多传感器多目标跟踪中的应用情况进行分析之后，提出了一种基于费歇信息增量的多传感器对多目标的分配方法．该方法较好地解决了传感器的组合分配问题，并能对目标的跟踪精度实施控制，仿真结果证明了该方法的有效性．     

基于多传感器信息融合的节能控制系统

目前公共场所的电器设备多、使用时间长、缺乏控制、耗电大,为此设计了一种基于多传感器信息融合的节能控制系统.从目前存在的问题、系统总体框架、实现及实验结果几方面进行了探讨.系统采用闭环控制,以传感器检测的信号为反馈信息,并行输入CPU中进行决策层的信息融合,再并行输出多路信号动态控制设备的运行,达到了节能目的,具有较高的准确性、快速性和稳定性.实验表明,该节能控制系统满足设计要求,已在某客运站房的节能控制中得到应用.     

一种利用多传感器进行目标识别的方法

针对传统单传感器目标识别利用目标信息有限、识别结果可靠性不高等缺陷 ,提出一种利用多传感器进行目标识别方法 ,这种方法充分利用了目标多重特征的信息 ,并考虑了不同特征信息在目标识别中的重要程度 ,从而得到更为接近实际效果的识别结果     

多传感器在线检测在高精度划线切割机器人系统中的应用

高精度型材划线切割机器人系统是应造船领域的型材的划线和切割而开发研制的 ,多传感器数据融合是该系统的关键技术之一 .因为造船中所使用的型材几乎完全都是变形的 ,为了提高划线和切割精度 ,在该系统中 ,建立了多传感器在线检测系统 ,引入了并行扩展式卡尔曼滤波的多传感器数据融合方法实现非线性的多传感器数据融合 .实验结果显示 ,多传感器数据融合可以有效的实现变形型材的在线检测 ,提高系统的测试精度和可靠性 ,该技术是实现型材的高精度划线和切割的保证     

多传感器信息融合测控体系在温室测控中的应用研究

对农作物生长环境的温度、湿度和光照等参数的实时监测和远程控制是农业生产现代化的重要手段,为此设计了一种以ARM嵌入式微处理器2410为硬件平台,结合Linux操作系统和ADS1.2集成开发环境,同时采用D-S证据理论和BP神经网络相结合算法的多传感器信息融合测控系统。BP神经网络提供一定数量的证据,D-S证据理论降低证据的不确定性,将这种基于二者的多传感器信息融合算法应用于农作物生长环境监控中,最终得到了在S1(T>30°)温度区间内93.47%的可信度,可见设计的基于多传感器信息融合的测控体系具有良好的应用前景。