A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization sch...A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node's location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm-Extended Monte Carlo Localization (Ext-MCL) is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.展开更多
We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatial...We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.展开更多
针对室内移动机器人运行时可能发生碰撞、打滑和被人为搬运等情况而导致定位失效的问题,提出了一种基于粒子群优化的移动机器人MCL(Monte Carlo Localization)全局定位算法。基于MCL算法框架,通过定义粒子有效数目的方式来识别测量更新...针对室内移动机器人运行时可能发生碰撞、打滑和被人为搬运等情况而导致定位失效的问题,提出了一种基于粒子群优化的移动机器人MCL(Monte Carlo Localization)全局定位算法。基于MCL算法框架,通过定义粒子有效数目的方式来识别测量更新后定位算法的准确性,并利用粒子群优化算法将粒子集的位置向观测概率高的方向调整,调整后粒子集的姿态调整依靠重要性采样完成粒子集的更新过程。在Matlab下的仿真和实验结果表明改进后的MCL全局定位算法在位姿估计失效后恢复定位的性能明显优于MCL算法,可为MCL算法应用于机器人全局定位提供依据。展开更多
粒子滤波是移动机器人蒙特卡罗定位(Monte Carlo localization,MCL)的核心环节.首先,针对粒子滤波过程的粒子退化问题,利用迭代Sigma点卡尔曼滤波来精确设计粒子滤波器的提议分布,以迭代更新方式将当前观测信息融入顺序重要性采样过程,...粒子滤波是移动机器人蒙特卡罗定位(Monte Carlo localization,MCL)的核心环节.首先,针对粒子滤波过程的粒子退化问题,利用迭代Sigma点卡尔曼滤波来精确设计粒子滤波器的提议分布,以迭代更新方式将当前观测信息融入顺序重要性采样过程,提出IUPF(Improved unscented particle filter)算法.然后,将IUPF与移动机器人MCL相结合,给出IUPF-MCL定位算法的实现细节.仿真结果表明,IUPF-MCL是一种精确鲁棒的移动机器人定位算法.展开更多
针对无线传感器网络(WSN)中的移动节点定位问题,提出了一种将反馈时间序列与蒙特卡洛相结合的定位算法TSMCL(Feedback Time Series-Based Monte Carlo)。该算法基于目标节点1跳范围内的邻居锚节点(至少3个)反馈信号的先后顺序,构建了节...针对无线传感器网络(WSN)中的移动节点定位问题,提出了一种将反馈时间序列与蒙特卡洛相结合的定位算法TSMCL(Feedback Time Series-Based Monte Carlo)。该算法基于目标节点1跳范围内的邻居锚节点(至少3个)反馈信号的先后顺序,构建了节点可能的初始采样区域R1,并以区域R1与蒙特卡洛采样区域R2的重叠区作为新的采样区域R,以进一步缩小采样范围、提高采样效率。仿真结果表明:与蒙特卡洛定位算法相比,提出的TSMCL算法能够减少约38%的定位误差,尤其当节点移动速度较高时,算法的收敛速度也得到了显著提升。展开更多
基金the National Natural Science Foundation of China (No.60671033)the Research Fund for the Doctoral Program of Higher Education (No.20060614015).
文摘A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node's location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm-Extended Monte Carlo Localization (Ext-MCL) is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.
基金Project(2018YFC1505401)supported by the National Key R&D Program of ChinaProject(41702310)supported by the National Natural Science Foundation of China+1 种基金Project(SKLGP2017K014)supported by the Foundation of State Key Laboratory of Geohazard Prevention and Geo-environment Protection,ChinaProject(2018JJ3644)supported by the Natural Science Foundation of Hunan Province,China
文摘We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.
文摘针对室内移动机器人运行时可能发生碰撞、打滑和被人为搬运等情况而导致定位失效的问题,提出了一种基于粒子群优化的移动机器人MCL(Monte Carlo Localization)全局定位算法。基于MCL算法框架,通过定义粒子有效数目的方式来识别测量更新后定位算法的准确性,并利用粒子群优化算法将粒子集的位置向观测概率高的方向调整,调整后粒子集的姿态调整依靠重要性采样完成粒子集的更新过程。在Matlab下的仿真和实验结果表明改进后的MCL全局定位算法在位姿估计失效后恢复定位的性能明显优于MCL算法,可为MCL算法应用于机器人全局定位提供依据。
文摘针对无线传感器网络(WSN)中的移动节点定位问题,提出了一种将反馈时间序列与蒙特卡洛相结合的定位算法TSMCL(Feedback Time Series-Based Monte Carlo)。该算法基于目标节点1跳范围内的邻居锚节点(至少3个)反馈信号的先后顺序,构建了节点可能的初始采样区域R1,并以区域R1与蒙特卡洛采样区域R2的重叠区作为新的采样区域R,以进一步缩小采样范围、提高采样效率。仿真结果表明:与蒙特卡洛定位算法相比,提出的TSMCL算法能够减少约38%的定位误差,尤其当节点移动速度较高时,算法的收敛速度也得到了显著提升。
