Since 2004,China has been dedicated to expanding economic integration with ASEAN countries,designating Guangxi Zhuang Autonomous Region as a hub to attract ASEAN member countries for greater coordination and economic ...Since 2004,China has been dedicated to expanding economic integration with ASEAN countries,designating Guangxi Zhuang Autonomous Region as a hub to attract ASEAN member countries for greater coordination and economic cooperation.Each year,Guangxi hosts the China-ASEAN Expo(CAEXPO)and related activities,providing a platform for strategic economic partners to meet,discuss,and explore ways to strengthen trade and investment cooperation.展开更多
在室内定位系统中,基于Wi-Fi技术的定位精度很大程度上依赖于信号的稳定,信号的多径效应与非视距(Non Line of Sight,NLOS)会增大定位误差。行人航位推算(Pedestrian Dead Reckoning,PDR)定位系统会因传感器自身误差与噪声产生累计误差...在室内定位系统中,基于Wi-Fi技术的定位精度很大程度上依赖于信号的稳定,信号的多径效应与非视距(Non Line of Sight,NLOS)会增大定位误差。行人航位推算(Pedestrian Dead Reckoning,PDR)定位系统会因传感器自身误差与噪声产生累计误差。针对上述问题,提出了一种改进的PDR与最小一乘法(Least Absolute Deviation,LAD)融合的室内定位算法。该算法基于模糊逻辑将PDR算法的步长固定参数改进为变量参数,同时根据LAD的定位结果对PDR进行周期性位置与拐点位置校正,选择扩展卡尔曼滤波(Extend Kalman Filter,EKF)将改进的PDR与LAD进行融合,以降低PDR的累计误差与LAD的突变误差,提高定位精度。实验结果表明:所提方法较其他方法具有更高的定位精度。展开更多
为了降低行人航位推算(Pedestrian dead reckoning,PDR)算法在进行井下人员定位时产生的累积误差,提出了一种基于PDR算法与伪平面技术的井下人员定位方法。首先,采用惯性导航传感器获取井下人员的步态信息,通过线性步长估计模型和四元...为了降低行人航位推算(Pedestrian dead reckoning,PDR)算法在进行井下人员定位时产生的累积误差,提出了一种基于PDR算法与伪平面技术的井下人员定位方法。首先,采用惯性导航传感器获取井下人员的步态信息,通过线性步长估计模型和四元数法实现步长估计和方向估计,利用PDR算法推算人员的位置;其次,使用井下人员活动区域以及预设的标记点构建伪平面,并将井下人员位置映射到伪平面坐标上,为降低PDR算法的累积误差做准备;最后,采用SVM进行井下人员活动检测,通过转弯活动判断其是否处于特殊标记点,将PDR解算的位置与伪平面内已知转弯位置标记点进行相关性分析,完成伪平面信息与工人位置的匹配,校准并更新PDR位置,降低累积误差。结果表明:井下工人在完成单个转弯活动过程中,传统PDR算法解算位置平均误差为0.98 m,而进行伪平面修正后平均误差降低到0.31 m;在完成区域性多活动过程中,采用伪平面技术修正后的PDR平均定位误差从1.08 m降低到0.38 m。因此,所提出的井下人员定位方法有效提高了PDR算法的定位精度。展开更多
The Dongsithouane National Production Forest (DNPF) is one of the largest natural forest areas in Savannakhet, Lao PDR, which has been a vital support for the local community’s livelihood, Recently, significant chang...The Dongsithouane National Production Forest (DNPF) is one of the largest natural forest areas in Savannakhet, Lao PDR, which has been a vital support for the local community’s livelihood, Recently, significant changes in land use and land cover (LULC) have been observed in this area, leading to a reduction of natural forests. There were two separate methods of this study: firstly, to identify LULC changes across three different periods, spectral imagery from the Landsat 5 Thematic Mapper (TM) for the years 2001 and 2011, and the Landsat 8 Operational Land Imager (OLI) for 2021 were used as the primary data sources. The satellite images were preprocessed for various forest classes, including pretreatment of the top of atmosphere reflectance by using QGIS software’s semi-automatic classification plug-in (SCP), and ArcGIS was used for post-classification. A supervised classification approach was applied to the satellite images from 2001, 2011, and 2021 to generate diverse maps of LULC. Secondly, a household survey dataset was used to investigate influential factors. Approximately 220 households were interviewed in order to collect socio-economic information (including data on population growth, increased business activities, location of the area, agriculture land expansion, and need for settlement land). Household survey data was analyzed by using SPSS. Descriptive statistics, including frequency distributions and percentages, were applied to observe characteristics. Additionally, a binary logistic regression model was used to analyze the socioeconomic factors related to LULC change in DNPF. Key findings indicated a decline in natural forest areas within the study site. Specifically, both dry dipterocarp forest (−11.35%) and mixed deciduous forest (−0.18%) decreased from 2001 to 2021. The overall accuracy of the LULC maps was 94%, 86%, and 89% for the years 2001, 2011, and 2021 respectively. In contrast, agricultural land increased significantly by 155.70%, while built-up land, and water bodies increased by 65.54% and 35.33%, respectively. The results also highlighted a significant increase in construction land, up to 65.54%. Furthermore, the study found a correlation between agricultural expansion and a reduction of forest areas, along with an increase in built-up land along the forest areas’ boundaries. Timber exploitation and charcoal production also contributed to the decline in forest cover. The logistic regression model identified significant determinants of LULC change, including the area’s location, agricultural land expansion, increased business activity, and the need for settlement land. These factors have influenced the management of DNPF. Urgent sustainable management practices and actions, including forest ecosystem protection, village agricultural zoning, water source and watershed protection and public awareness, are required to preserve the forest areas of DNPF.展开更多
针对在狭长空间下传统的行人航迹推算(Pedestrian Dead Reckoning,PDR)方法易受传感器扰动与环境干扰,导致传感器数据存在误差累积无法实现行人位置精确估计的问题,结合室内走廊狭长空间典型场景下北斗伪卫星(Pseudosatellite,PL)的信...针对在狭长空间下传统的行人航迹推算(Pedestrian Dead Reckoning,PDR)方法易受传感器扰动与环境干扰,导致传感器数据存在误差累积无法实现行人位置精确估计的问题,结合室内走廊狭长空间典型场景下北斗伪卫星(Pseudosatellite,PL)的信号特征,提出了一种基于北斗信标辅助的PDR狭长空间定位方法。通过提取空间下确定性位置PL观测量数据特征,建立了数据特征与空间位置的指纹位置对应关系。设计了一种北斗PL与PDR组合的拐点检测方法。以北斗信标节点为基础,结合PDR适用范围大与应用性强的特性,将方向信息组合到卡尔曼滤波算法中完成设计。通过实测验证,与PDR定位方法相比,在室内狭长空间情况下组合系统的均方根误差(Root Mean Squared Error,RMSE)定位精度提高了54%。展开更多
文摘Since 2004,China has been dedicated to expanding economic integration with ASEAN countries,designating Guangxi Zhuang Autonomous Region as a hub to attract ASEAN member countries for greater coordination and economic cooperation.Each year,Guangxi hosts the China-ASEAN Expo(CAEXPO)and related activities,providing a platform for strategic economic partners to meet,discuss,and explore ways to strengthen trade and investment cooperation.
文摘在室内定位系统中,基于Wi-Fi技术的定位精度很大程度上依赖于信号的稳定,信号的多径效应与非视距(Non Line of Sight,NLOS)会增大定位误差。行人航位推算(Pedestrian Dead Reckoning,PDR)定位系统会因传感器自身误差与噪声产生累计误差。针对上述问题,提出了一种改进的PDR与最小一乘法(Least Absolute Deviation,LAD)融合的室内定位算法。该算法基于模糊逻辑将PDR算法的步长固定参数改进为变量参数,同时根据LAD的定位结果对PDR进行周期性位置与拐点位置校正,选择扩展卡尔曼滤波(Extend Kalman Filter,EKF)将改进的PDR与LAD进行融合,以降低PDR的累计误差与LAD的突变误差,提高定位精度。实验结果表明:所提方法较其他方法具有更高的定位精度。
文摘The Dongsithouane National Production Forest (DNPF) is one of the largest natural forest areas in Savannakhet, Lao PDR, which has been a vital support for the local community’s livelihood, Recently, significant changes in land use and land cover (LULC) have been observed in this area, leading to a reduction of natural forests. There were two separate methods of this study: firstly, to identify LULC changes across three different periods, spectral imagery from the Landsat 5 Thematic Mapper (TM) for the years 2001 and 2011, and the Landsat 8 Operational Land Imager (OLI) for 2021 were used as the primary data sources. The satellite images were preprocessed for various forest classes, including pretreatment of the top of atmosphere reflectance by using QGIS software’s semi-automatic classification plug-in (SCP), and ArcGIS was used for post-classification. A supervised classification approach was applied to the satellite images from 2001, 2011, and 2021 to generate diverse maps of LULC. Secondly, a household survey dataset was used to investigate influential factors. Approximately 220 households were interviewed in order to collect socio-economic information (including data on population growth, increased business activities, location of the area, agriculture land expansion, and need for settlement land). Household survey data was analyzed by using SPSS. Descriptive statistics, including frequency distributions and percentages, were applied to observe characteristics. Additionally, a binary logistic regression model was used to analyze the socioeconomic factors related to LULC change in DNPF. Key findings indicated a decline in natural forest areas within the study site. Specifically, both dry dipterocarp forest (−11.35%) and mixed deciduous forest (−0.18%) decreased from 2001 to 2021. The overall accuracy of the LULC maps was 94%, 86%, and 89% for the years 2001, 2011, and 2021 respectively. In contrast, agricultural land increased significantly by 155.70%, while built-up land, and water bodies increased by 65.54% and 35.33%, respectively. The results also highlighted a significant increase in construction land, up to 65.54%. Furthermore, the study found a correlation between agricultural expansion and a reduction of forest areas, along with an increase in built-up land along the forest areas’ boundaries. Timber exploitation and charcoal production also contributed to the decline in forest cover. The logistic regression model identified significant determinants of LULC change, including the area’s location, agricultural land expansion, increased business activity, and the need for settlement land. These factors have influenced the management of DNPF. Urgent sustainable management practices and actions, including forest ecosystem protection, village agricultural zoning, water source and watershed protection and public awareness, are required to preserve the forest areas of DNPF.
文摘针对在狭长空间下传统的行人航迹推算(Pedestrian Dead Reckoning,PDR)方法易受传感器扰动与环境干扰,导致传感器数据存在误差累积无法实现行人位置精确估计的问题,结合室内走廊狭长空间典型场景下北斗伪卫星(Pseudosatellite,PL)的信号特征,提出了一种基于北斗信标辅助的PDR狭长空间定位方法。通过提取空间下确定性位置PL观测量数据特征,建立了数据特征与空间位置的指纹位置对应关系。设计了一种北斗PL与PDR组合的拐点检测方法。以北斗信标节点为基础,结合PDR适用范围大与应用性强的特性,将方向信息组合到卡尔曼滤波算法中完成设计。通过实测验证,与PDR定位方法相比,在室内狭长空间情况下组合系统的均方根误差(Root Mean Squared Error,RMSE)定位精度提高了54%。
