It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a signific...It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.展开更多
The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot. A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensor...The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot. A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensors for mobile robot exploration. At first, the invariant feature based images stitching approach is adopted to form a panoramic image according to Kinect visual information, and the translation between Kinect depth information and obstacle distance information is performed to obtain virtual LIDAR data. Then, the semantic classifier is designed by using convolutional neural networks （CNN） for indoor place eategorization based on Kinect visual observations with panoramic view. At last, a frontier-based exploration method is applied to carry out indoor autonomous exploration of mo- bile robots, which integrates the CNN-based categorization approach. The proposed method has been implemented and tested on a real robot, and experiment results demonstrate the approach effective- ness on solving the semantic categorization problem for mobile robot exploration.展开更多
文摘在使用毫米波雷达进行室内人员信息检测时,其信号处理阶段采用的静态杂波滤除算法有效地滤除了检测区域中包括墙壁、地面、桌椅等在内的静止目标,实现了对运动人员的检测,但同时会导致静止人员被漏检.为此提出按照径向速度把点云数据划分为动态数据和静态数据,先剔除动态数据,然后累积剩余的静态数据.在达到指定的累积帧数时,进行密度聚类,以簇的数量作为人员的数量,簇的中心坐标作为人员的位置.通过实验,验证了所提出方法的有效性,在室内办公场景下,人员数量统计平均绝对误差为0.81,人员位置估计均方根误差为0.1 m.
基金supported by National Basic Research Program of China (NO 2012CB316002)China’s 863 Project (NO 2014AA01A703)+2 种基金National Major Projec (NO. 2014ZX03003002-002)Program for New Century Excellent Talents in University (NCET-13-0321)Tsinghua University Initiative Scientific Research Program (2011THZ02-2)
文摘It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.
基金Supported by the National Key Basic Research Program of China(No.2013CB035503)
文摘The ability of achieving a semantic understanding of workspaces is an important capability for mobile robot. A method is proposed to categorize different places in a typical indoor environment by using a Kinect sensors for mobile robot exploration. At first, the invariant feature based images stitching approach is adopted to form a panoramic image according to Kinect visual information, and the translation between Kinect depth information and obstacle distance information is performed to obtain virtual LIDAR data. Then, the semantic classifier is designed by using convolutional neural networks （CNN） for indoor place eategorization based on Kinect visual observations with panoramic view. At last, a frontier-based exploration method is applied to carry out indoor autonomous exploration of mo- bile robots, which integrates the CNN-based categorization approach. The proposed method has been implemented and tested on a real robot, and experiment results demonstrate the approach effective- ness on solving the semantic categorization problem for mobile robot exploration.
