为解决移动无线传感器网络中节点连通性较弱的问题,提出一种包含不同移动性节点的无线传感器网络提升移动节点连通性的保障时隙(GTS,guaranteed time slot)分配策略。首先,采用Kalman滤波预测模型得到用户下一阶段位置;接下来,引入一种...为解决移动无线传感器网络中节点连通性较弱的问题,提出一种包含不同移动性节点的无线传感器网络提升移动节点连通性的保障时隙(GTS,guaranteed time slot)分配策略。首先,采用Kalman滤波预测模型得到用户下一阶段位置;接下来,引入一种考虑速度、方向和相对移动性的节点移动程度界定方法,并在此基础上进行GTS预约优先级的初步确定;随后,根据移动节点对所预约时隙的使用反馈情况自适应调整预约优先级;最后,根据节点的优先级决定GTS时隙的使用顺序及额外预留时隙的使用权。仿真结果显示,提出的分配策略在具有不同移动性节点的网络中,能够提高移动节点接入的成功率,保证较低的分组平均传输时延及较高的分组投递率。此外,采用基于反馈机制的自适应预约优先级调整策略能够显著增加整个网络中已分配时隙的正确使用率。展开更多
A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of ...A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.展开更多
文摘为解决移动无线传感器网络中节点连通性较弱的问题,提出一种包含不同移动性节点的无线传感器网络提升移动节点连通性的保障时隙(GTS,guaranteed time slot)分配策略。首先,采用Kalman滤波预测模型得到用户下一阶段位置;接下来,引入一种考虑速度、方向和相对移动性的节点移动程度界定方法,并在此基础上进行GTS预约优先级的初步确定;随后,根据移动节点对所预约时隙的使用反馈情况自适应调整预约优先级;最后,根据节点的优先级决定GTS时隙的使用顺序及额外预留时隙的使用权。仿真结果显示,提出的分配策略在具有不同移动性节点的网络中,能够提高移动节点接入的成功率,保证较低的分组平均传输时延及较高的分组投递率。此外,采用基于反馈机制的自适应预约优先级调整策略能够显著增加整个网络中已分配时隙的正确使用率。
基金Supported by the National Natural Science Foundation of China(62376214)the Natural Science Basic Research Program of Shaanxi(2023-JC-YB-533)Foundation of Ministry of Education Key Lab.of Cognitive Radio and Information Processing(Guilin University of Electronic Technology)(CRKL200203)。
文摘A modified multiple-component scattering power decomposition for analyzing polarimetric synthetic aperture radar(PolSAR)data is proposed.The modified decomposition involves two distinct steps.Firstly,ei⁃genvectors of the coherency matrix are used to modify the scattering models.Secondly,the entropy and anisotro⁃py of targets are used to improve the volume scattering power.With the guarantee of high double-bounce scatter⁃ing power in the urban areas,the proposed algorithm effectively improves the volume scattering power of vegeta⁃tion areas.The efficacy of the modified multiple-component scattering power decomposition is validated using ac⁃tual AIRSAR PolSAR data.The scattering power obtained through decomposing the original coherency matrix and the coherency matrix after orientation angle compensation is compared with three algorithms.Results from the experiment demonstrate that the proposed decomposition yields more effective scattering power for different PolSAR data sets.
