为了提高三维空间位置定位算法的性能和准确性,提出了以3个基站定位原理为基础的基于几何约束关系的ML模型.通过设计基站位置、到达时间(time of arrival,TOA)数值、手持终端位置之间的几何关系,估计得到手持终端的全部可能位置,通过最...为了提高三维空间位置定位算法的性能和准确性,提出了以3个基站定位原理为基础的基于几何约束关系的ML模型.通过设计基站位置、到达时间(time of arrival,TOA)数值、手持终端位置之间的几何关系,估计得到手持终端的全部可能位置,通过最大似然算法,以最大可能的估计值确定为定位结果.经过仿真计算验证了算法能有效抑制非视距环境(non-line of Sight,NLOS)误差和测量误差,并且能够得到最佳估计值,具有计算量小、精度高等优点.仿真结果表明了该方法的有效性,在各类室内定位系统中具有很强的实用性.展开更多
基于WACCM+DART(Whole Atmosphere Community Climate Model,Data Assimilation Research Test-Bed)临近空间资料同化预报系统,以2016年2月的一次平流层爆发性增温(SSW)事件为例,开展了临近空间SABER(Sounding of the Atmosphere using ...基于WACCM+DART(Whole Atmosphere Community Climate Model,Data Assimilation Research Test-Bed)临近空间资料同化预报系统,以2016年2月的一次平流层爆发性增温(SSW)事件为例,开展了临近空间SABER(Sounding of the Atmosphere using Broadband Emission Radiometry)和MLS(Microwave Limb Sounder)温度观测资料集合滤波同化试验.结果表明:同化SABER和MLS温度观测资料可显著降低WACCM模式在中间层和平流层中上部(0.001~10 hPa)大气温度场的预报误差,改善CR试验在SSW发生时中间层变冷现象偏强、纬向风场首次发生反转的层次偏低以及增温恢复阶段0.1~10 hPa的东风层提前消退、纬向风速偏大、平流层顶位置偏高等现象.基于ERA5(The Fifth Generation of ECMWF Reanalyses)再分析资料的检验表明:同化SABER和MLS温度资料明显有利于减小北半球高纬度地区(60°-90°N)平流层中上层和下中间层(0.1~14 hPa)纬向风场以及平流层和中间层中下层(0.01~100 hPa)温度场的分析误差;同化低层大气观测也有利于减小0.1~14hPa纬向风场和0.01~100 hPa温度场的分析误差,但是不如同化SABER和MLS温度资料对临近空间纬向风场和温度场分析误差的改善效果显著.展开更多
When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model ...When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.展开更多
文摘为了提高三维空间位置定位算法的性能和准确性,提出了以3个基站定位原理为基础的基于几何约束关系的ML模型.通过设计基站位置、到达时间(time of arrival,TOA)数值、手持终端位置之间的几何关系,估计得到手持终端的全部可能位置,通过最大似然算法,以最大可能的估计值确定为定位结果.经过仿真计算验证了算法能有效抑制非视距环境(non-line of Sight,NLOS)误差和测量误差,并且能够得到最佳估计值,具有计算量小、精度高等优点.仿真结果表明了该方法的有效性,在各类室内定位系统中具有很强的实用性.
文摘基于WACCM+DART(Whole Atmosphere Community Climate Model,Data Assimilation Research Test-Bed)临近空间资料同化预报系统,以2016年2月的一次平流层爆发性增温(SSW)事件为例,开展了临近空间SABER(Sounding of the Atmosphere using Broadband Emission Radiometry)和MLS(Microwave Limb Sounder)温度观测资料集合滤波同化试验.结果表明:同化SABER和MLS温度观测资料可显著降低WACCM模式在中间层和平流层中上部(0.001~10 hPa)大气温度场的预报误差,改善CR试验在SSW发生时中间层变冷现象偏强、纬向风场首次发生反转的层次偏低以及增温恢复阶段0.1~10 hPa的东风层提前消退、纬向风速偏大、平流层顶位置偏高等现象.基于ERA5(The Fifth Generation of ECMWF Reanalyses)再分析资料的检验表明:同化SABER和MLS温度资料明显有利于减小北半球高纬度地区(60°-90°N)平流层中上层和下中间层(0.1~14 hPa)纬向风场以及平流层和中间层中下层(0.01~100 hPa)温度场的分析误差;同化低层大气观测也有利于减小0.1~14hPa纬向风场和0.01~100 hPa温度场的分析误差,但是不如同化SABER和MLS温度资料对临近空间纬向风场和温度场分析误差的改善效果显著.
文摘When the distribution of the sources cannot be estimated accurately, the ICA algorithms failed to separate the mixtures blindly. The generalized Gaussian model （GGM） is presented in ICA algorithm since it can model non- Ganssian statistical structure of different source signals easily. By inferring only one parameter, a wide class of statistical distributions can be characterized. By using maximum likelihood （ML） approach and natural gradient descent, the learning rules of blind source separation （BSS） based on GGM are presented. The experiment of the ship-radiated noise demonstrates that the GGM can model the distributions of the ship-radiated noise and sea noise efficiently, and the learning rules based on GGM gives more successful separation results after comparing it with several conventional methods such as high order cumnlants and Gaussian mixture density function.
