In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into s...In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.展开更多
针对传统阵列天线来波方向(Direction of Arrival,DOA)估计算法需要准确获取信源数量的问题,提出了一种未知信源数量类多重信号分类(Multiple Signal Classification,MUSIC)DOA估计方法。首先根据阵列天线的多个快拍数据估计输入信号自...针对传统阵列天线来波方向(Direction of Arrival,DOA)估计算法需要准确获取信源数量的问题,提出了一种未知信源数量类多重信号分类(Multiple Signal Classification,MUSIC)DOA估计方法。首先根据阵列天线的多个快拍数据估计输入信号自相关矩阵;其次对信号自相关矩阵进行特征值分解,并使用重构相关矩阵的方式实现信号分量的抑制;最后结合传统MUSIC谱估计算法实现未知信源数量条件下的DOA估计。仿真实验表明,所提算法的复杂度较低,且DOA估计误差性能接近传统MUSIC算法。展开更多
基金supported in part by National Natural Science Foundation of China under Grants(61525101,61227801 and 61601055)in part by the National Key Technology R&D Program of China under Grant 2015ZX03002008
文摘In this paper,a space-time correlation based fast regional spectrum sensing(RSS)scheme is proposed to reduce the time and energy consumption of traditional spatial spectrum sensing. The target region is divided into small meshes,and all meshes are clustered into highly related groups using the spatial correlation among them. In each group,some representative meshes are selected as detecting meshes(DMs)using a multi-center mesh(MCM)clustering algorithm,while other meshes(EMs)are estimated according to their correlations with DMs and the Markov modeled dependence on history by MAP principle. Thus,detecting fewer meshes saves the sensing consumption. Since two independent estimation processes may provide contradictory results,minimum entropy principle is adopted to merge the results. Tested with data acquired by radio environment mapping measurement conducted in the downtown Beijing,our scheme is capable to reduce the consumption of traditional sensing method with acceptable sensing performance.
文摘针对传统阵列天线来波方向(Direction of Arrival,DOA)估计算法需要准确获取信源数量的问题,提出了一种未知信源数量类多重信号分类(Multiple Signal Classification,MUSIC)DOA估计方法。首先根据阵列天线的多个快拍数据估计输入信号自相关矩阵;其次对信号自相关矩阵进行特征值分解,并使用重构相关矩阵的方式实现信号分量的抑制;最后结合传统MUSIC谱估计算法实现未知信源数量条件下的DOA估计。仿真实验表明,所提算法的复杂度较低,且DOA估计误差性能接近传统MUSIC算法。
