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
The Local Monte Carlo(LMC)method is used to solve the problems of deep penetration and long time in the neutronics calculation of the radial neutron camera(RNC)diagnostic system on the experimental advanced supercondu...The Local Monte Carlo(LMC)method is used to solve the problems of deep penetration and long time in the neutronics calculation of the radial neutron camera(RNC)diagnostic system on the experimental advanced superconducting tokamak(EAST),and the radiation distribution of the RNC and the neutron flux at the detector positions of each channel are obtained.Compared with the results calculated by the global variance reduction method,it is shown that the LMC calculation is reliable within the reasonable error range.The calculation process of LMC is analyzed in detail,and the transport process of radiation particles is simulated in two steps.In the first step,an integrated neutronics model considering the complex window environment and a neutron source model based on EAST plasma shape are used to support the calculation.The particle information on the equivalent surface is analyzed to evaluate the rationality of settings of equivalent surface source and boundary.Based on the characteristic that only a local geometric model is needed in the second step,it is shown that the LMC is an advantageous calculation method for the nuclear shielding design of tokamak diagnostic systems.展开更多
提出了一种基于Monte Carlo方法的多机器人自定位方法.该方法在机器人进行自定位时,对用来估计机器人位置的MCL(Monte Carlo Localization)粒子空间进行栅格划分,然后采用可变栅格法获得能代表所有粒子整体特性的特征粒子集.因为特征粒...提出了一种基于Monte Carlo方法的多机器人自定位方法.该方法在机器人进行自定位时,对用来估计机器人位置的MCL(Monte Carlo Localization)粒子空间进行栅格划分,然后采用可变栅格法获得能代表所有粒子整体特性的特征粒子集.因为特征粒子的数量较粒子总数大大减少,该方法能避免直接将Monte Carlo方法应用于多机器人定位中产生的维数灾的问题,可以在保证精度的情况下降低运算复杂度.仿真结果表明,该方法能较好地满足多机器人自定位的要求.展开更多
环境因素导致无线传感器网络定位存在噪声影响,实质上是非平滑的非线性问题。针对传统粒子滤波算法在处理该问题时精度不高的缺点,提出一种基于后验泊松分布的Monte Carlo Gaussian重采样粒子滤波算法的无线传感器网络定位算法。基于粒...环境因素导致无线传感器网络定位存在噪声影响,实质上是非平滑的非线性问题。针对传统粒子滤波算法在处理该问题时精度不高的缺点,提出一种基于后验泊松分布的Monte Carlo Gaussian重采样粒子滤波算法的无线传感器网络定位算法。基于粒子滤波算法,借鉴扩展卡尔曼滤波算法采用近似后验高斯分布思想,设计了后验泊松分布Monte Carlo Gaussian重采样粒子滤波器。采用该滤波器设计实现了无线传感器网络定位算法,解决了非平滑非线性的噪声干扰定位问题。分别对滤波器和定位算法的性能进行了对比仿真实验,结果验证了所提算法的有效性。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support and help in this research.This work was supported by Users with Excellence Program of Hefei Science Center CAS(No.2020HSC-UE012)Comprehensive Research Facility for Fusion Technology Program of China(No.2018-000052-73-01-001228)National Natural Science Foundation of China(No.11605241)。
文摘The Local Monte Carlo(LMC)method is used to solve the problems of deep penetration and long time in the neutronics calculation of the radial neutron camera(RNC)diagnostic system on the experimental advanced superconducting tokamak(EAST),and the radiation distribution of the RNC and the neutron flux at the detector positions of each channel are obtained.Compared with the results calculated by the global variance reduction method,it is shown that the LMC calculation is reliable within the reasonable error range.The calculation process of LMC is analyzed in detail,and the transport process of radiation particles is simulated in two steps.In the first step,an integrated neutronics model considering the complex window environment and a neutron source model based on EAST plasma shape are used to support the calculation.The particle information on the equivalent surface is analyzed to evaluate the rationality of settings of equivalent surface source and boundary.Based on the characteristic that only a local geometric model is needed in the second step,it is shown that the LMC is an advantageous calculation method for the nuclear shielding design of tokamak diagnostic systems.
文摘提出了一种基于Monte Carlo方法的多机器人自定位方法.该方法在机器人进行自定位时,对用来估计机器人位置的MCL(Monte Carlo Localization)粒子空间进行栅格划分,然后采用可变栅格法获得能代表所有粒子整体特性的特征粒子集.因为特征粒子的数量较粒子总数大大减少,该方法能避免直接将Monte Carlo方法应用于多机器人定位中产生的维数灾的问题,可以在保证精度的情况下降低运算复杂度.仿真结果表明,该方法能较好地满足多机器人自定位的要求.
文摘环境因素导致无线传感器网络定位存在噪声影响,实质上是非平滑的非线性问题。针对传统粒子滤波算法在处理该问题时精度不高的缺点,提出一种基于后验泊松分布的Monte Carlo Gaussian重采样粒子滤波算法的无线传感器网络定位算法。基于粒子滤波算法,借鉴扩展卡尔曼滤波算法采用近似后验高斯分布思想,设计了后验泊松分布Monte Carlo Gaussian重采样粒子滤波器。采用该滤波器设计实现了无线传感器网络定位算法,解决了非平滑非线性的噪声干扰定位问题。分别对滤波器和定位算法的性能进行了对比仿真实验,结果验证了所提算法的有效性。
