波束内目标与诱饵的参数估计是导引头正确实现目标分选、完成波束指向调整与精确跟踪的必要条件。目标与诱饵的"紧密接近"导致接收回波混叠,使得常规参数测量与估计方法失效。基于实际采样处理中目标回波能量会"溢出&qu...波束内目标与诱饵的参数估计是导引头正确实现目标分选、完成波束指向调整与精确跟踪的必要条件。目标与诱饵的"紧密接近"导致接收回波混叠,使得常规参数测量与估计方法失效。基于实际采样处理中目标回波能量会"溢出"到相邻匹配滤波采样点这一信号模型,通过贝叶斯原理从观测的条件似然以及未知参数的先验分布获取待估计参数的后验概率分布,采用Markov Chain Monte Carlo(MCMC)方法中的Metropolis-Hastings(M-H)抽样算法联合估计目标与诱饵的相关参数,并根据拖曳式诱饵干扰对抗的特点对M-H抽样进行了改进。各种典型干扰条件及动态攻击场景下的仿真试验表明了本文方法的有效性。展开更多
由于RFID设备固有特性的限制和环境噪声的影响,造成RFID原始数据的不确定,进一步引起标签位置信息的不准确,严重影响目标对象识别、定位以及跟踪与追溯等业务功能。在物流仓库中基于实际采样处理的电子标签可能溢出到相邻阅读器识别区...由于RFID设备固有特性的限制和环境噪声的影响,造成RFID原始数据的不确定,进一步引起标签位置信息的不准确,严重影响目标对象识别、定位以及跟踪与追溯等业务功能。在物流仓库中基于实际采样处理的电子标签可能溢出到相邻阅读器识别区域这一冗余特点,利用贝叶斯概率推断模型并辅以最小熵的阅读器识别模型,从RFID不确定数据流中捕获标签数据的位置概率分布,采用自适应Markov Chain Monte Carlo(MCMC)方法联合估计物流仓库中RFID数据与标签符号位置参数。最后,利用仿真实验对本算法的有效性和准确性进行了验证。展开更多
提出了一种基于双旋转单偶极子阵列的联合谱参数估计算法.通过旋转阵元及修正极化多重信号分类(multiple signal classification,MUSIC)算法中谱函数的导向矢量,便能实现用两阵元进行多目标测向,回避了传统算法中要求信号源个数小于阵...提出了一种基于双旋转单偶极子阵列的联合谱参数估计算法.通过旋转阵元及修正极化多重信号分类(multiple signal classification,MUSIC)算法中谱函数的导向矢量,便能实现用两阵元进行多目标测向,回避了传统算法中要求信号源个数小于阵元数的问题.仿真结果表明:在小快拍数、低信噪比的影响下,该算法在测向性能上优于阵元数为6的极化空间谱估计算法,且所用通道数少,可降低成本.该算法所述的单偶极子阵列可用任意极化敏感天线单元或组合进行构造,可移植性较强.展开更多
Considering the dependent relationship among wave height,wind speed,and current velocity,we construct novel trivariate joint probability distributions via Archimedean copula functions.Total 30-year data of wave height...Considering the dependent relationship among wave height,wind speed,and current velocity,we construct novel trivariate joint probability distributions via Archimedean copula functions.Total 30-year data of wave height,wind speed,and current velocity in the Bohai Sea are hindcast and sampled for case study.Four kinds of distributions,namely,Gumbel distribution,lognormal distribution,Weibull distribution,and Pearson Type III distribution,are candidate models for marginal distributions of wave height,wind speed,and current velocity.The Pearson Type III distribution is selected as the optimal model.Bivariate and trivariate probability distributions of these environmental conditions are established based on four bivariate and trivariate Archimedean copulas,namely,Clayton,Frank,Gumbel-Hougaard,and Ali-Mikhail-Haq copulas.These joint probability models can maximize marginal information and the dependence among the three variables.The design return values of these three variables can be obtained by three methods:univariate probability,conditional probability,and joint probability.The joint return periods of different load combinations are estimated by the proposed models.Platform responses(including base shear,overturning moment,and deck displacement) are further calculated.For the same return period,the design values of wave height,wind speed,and current velocity obtained by the conditional and joint probability models are much smaller than those by univariate probability.Considering the dependence among variables,the multivariate probability distributions provide close design parameters to actual sea state for ocean platform design.展开更多
文摘波束内目标与诱饵的参数估计是导引头正确实现目标分选、完成波束指向调整与精确跟踪的必要条件。目标与诱饵的"紧密接近"导致接收回波混叠,使得常规参数测量与估计方法失效。基于实际采样处理中目标回波能量会"溢出"到相邻匹配滤波采样点这一信号模型,通过贝叶斯原理从观测的条件似然以及未知参数的先验分布获取待估计参数的后验概率分布,采用Markov Chain Monte Carlo(MCMC)方法中的Metropolis-Hastings(M-H)抽样算法联合估计目标与诱饵的相关参数,并根据拖曳式诱饵干扰对抗的特点对M-H抽样进行了改进。各种典型干扰条件及动态攻击场景下的仿真试验表明了本文方法的有效性。
文摘由于RFID设备固有特性的限制和环境噪声的影响,造成RFID原始数据的不确定,进一步引起标签位置信息的不准确,严重影响目标对象识别、定位以及跟踪与追溯等业务功能。在物流仓库中基于实际采样处理的电子标签可能溢出到相邻阅读器识别区域这一冗余特点,利用贝叶斯概率推断模型并辅以最小熵的阅读器识别模型,从RFID不确定数据流中捕获标签数据的位置概率分布,采用自适应Markov Chain Monte Carlo(MCMC)方法联合估计物流仓库中RFID数据与标签符号位置参数。最后,利用仿真实验对本算法的有效性和准确性进行了验证。
文摘提出了一种基于双旋转单偶极子阵列的联合谱参数估计算法.通过旋转阵元及修正极化多重信号分类(multiple signal classification,MUSIC)算法中谱函数的导向矢量,便能实现用两阵元进行多目标测向,回避了传统算法中要求信号源个数小于阵元数的问题.仿真结果表明:在小快拍数、低信噪比的影响下,该算法在测向性能上优于阵元数为6的极化空间谱估计算法,且所用通道数少,可降低成本.该算法所述的单偶极子阵列可用任意极化敏感天线单元或组合进行构造,可移植性较强.
基金partially supported by the National Natural Science Foundation of China(No.51479183)the National Key Research and Development Program,China(Nos.2016YFC0302301 and 2016YFC0803401)the Fundamental Research Funds for the Central University(No.201564003)
文摘Considering the dependent relationship among wave height,wind speed,and current velocity,we construct novel trivariate joint probability distributions via Archimedean copula functions.Total 30-year data of wave height,wind speed,and current velocity in the Bohai Sea are hindcast and sampled for case study.Four kinds of distributions,namely,Gumbel distribution,lognormal distribution,Weibull distribution,and Pearson Type III distribution,are candidate models for marginal distributions of wave height,wind speed,and current velocity.The Pearson Type III distribution is selected as the optimal model.Bivariate and trivariate probability distributions of these environmental conditions are established based on four bivariate and trivariate Archimedean copulas,namely,Clayton,Frank,Gumbel-Hougaard,and Ali-Mikhail-Haq copulas.These joint probability models can maximize marginal information and the dependence among the three variables.The design return values of these three variables can be obtained by three methods:univariate probability,conditional probability,and joint probability.The joint return periods of different load combinations are estimated by the proposed models.Platform responses(including base shear,overturning moment,and deck displacement) are further calculated.For the same return period,the design values of wave height,wind speed,and current velocity obtained by the conditional and joint probability models are much smaller than those by univariate probability.Considering the dependence among variables,the multivariate probability distributions provide close design parameters to actual sea state for ocean platform design.
