提出一种基于超宽带(ultra wideband,UWB)信号到达时间估计(time of arrival,TOA)/到达角度估计(angle of arrival,AOA)联合估计的无线传感器网络(wireless sensor networks,WSNs)定位方案,只需要一个参考节点就可以实现对其他传感器节...提出一种基于超宽带(ultra wideband,UWB)信号到达时间估计(time of arrival,TOA)/到达角度估计(angle of arrival,AOA)联合估计的无线传感器网络(wireless sensor networks,WSNs)定位方案,只需要一个参考节点就可以实现对其他传感器节点的2D相对定位,并且不需要时钟同步,适合于传感器网络节点的低成本设计需求.利用往返时间(round trip time,RTT)进行TOA估计,给出了基于多径检测的TOA估计算法;利用到达时间差估计(time difference of arrival,TDOA)进行AOA估计,因而无需借助复杂的天线波束赋形技术.同时,分析了定位误差模型对定位性能的影响,并通过IEEE802.15.4a信道下的仿真实验进行了验证,结果表明了所提方案的有效性.展开更多
针对多星定位系统对地面静态目标的无源定位误差分析问题,运用Fisher信息矩阵、Taylor级数、矩阵理论和统计理论,综合考虑时差、频差、卫星位置误差以及卫星速度误差,推导了到达时间差(time difference of arrival,TDOA)/到达频率差(fre...针对多星定位系统对地面静态目标的无源定位误差分析问题,运用Fisher信息矩阵、Taylor级数、矩阵理论和统计理论,综合考虑时差、频差、卫星位置误差以及卫星速度误差,推导了到达时间差(time difference of arrival,TDOA)/到达频率差(frequency difference of arrival,FDOA)联合定位误差克拉美·罗界(Cramer-Rao lower bound,CRLB)的简单表达式,以及三星单独TDOA定位误差的CRLB,进而给出了避免TDOA定位盲区的良好卫星构型设计的充分条件.理论分析与仿真结果表明:在单独TDOA定位场景下良好的构型能完全消除定位盲区,定位精度随主星-星下点连线与主星-副星连线的夹角逼近90°而逐渐提高;通过引入FDOA与TDOA联合定位也能有效避免定位盲区,提高定位精度.展开更多
Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate loca...Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.展开更多
The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measu...The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.展开更多
文摘提出一种基于超宽带(ultra wideband,UWB)信号到达时间估计(time of arrival,TOA)/到达角度估计(angle of arrival,AOA)联合估计的无线传感器网络(wireless sensor networks,WSNs)定位方案,只需要一个参考节点就可以实现对其他传感器节点的2D相对定位,并且不需要时钟同步,适合于传感器网络节点的低成本设计需求.利用往返时间(round trip time,RTT)进行TOA估计,给出了基于多径检测的TOA估计算法;利用到达时间差估计(time difference of arrival,TDOA)进行AOA估计,因而无需借助复杂的天线波束赋形技术.同时,分析了定位误差模型对定位性能的影响,并通过IEEE802.15.4a信道下的仿真实验进行了验证,结果表明了所提方案的有效性.
文摘针对多星定位系统对地面静态目标的无源定位误差分析问题,运用Fisher信息矩阵、Taylor级数、矩阵理论和统计理论,综合考虑时差、频差、卫星位置误差以及卫星速度误差,推导了到达时间差(time difference of arrival,TDOA)/到达频率差(frequency difference of arrival,FDOA)联合定位误差克拉美·罗界(Cramer-Rao lower bound,CRLB)的简单表达式,以及三星单独TDOA定位误差的CRLB,进而给出了避免TDOA定位盲区的良好卫星构型设计的充分条件.理论分析与仿真结果表明:在单独TDOA定位场景下良好的构型能完全消除定位盲区,定位精度随主星-星下点连线与主星-副星连线的夹角逼近90°而逐渐提高;通过引入FDOA与TDOA联合定位也能有效避免定位盲区,提高定位精度.
基金Project(2020A1515010718)supported by the Basic and Applied Basic Research Foundation of Guangdong Province,China。
文摘Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.
基金Supported by the National Natural Science Foundation of China （No. 60736006 and 60875019）
文摘The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.
