ID 原文 译文
923 首先，文中给出容积卡尔曼高斯混合势均衡多目标多伯努利滤波器(CK-GM-CBMeMBerF)的实现形式，并提取高斯混合分量近似多伯努利密度。 First, This paper gives the implementation of the Cubature Kalman Gaussian Mixture Cardinality Balanced Multi-target Multi-BernoulliFilter (CK-GMCBMeMBerF), and extracts the Gaussian mixture component to approximate multi-Bernoulli density.
924 然后，研究两个高斯混合之间的柯西施瓦兹(Cauchy-Schwarz)散度的求取，推导多目标概率密度变化所对应的信息增益，并以此为基础提出相应的传感器控制策略。 In ad-dition, we study the solution of the Cauchy-Schwarz divergence between the two Gaussian mixture distributions, and derive the information gain corresponding to the change of multi-target probability density. Then, the corresponding sensor controlstrategy is proposed.
925 此外，结合 CK-GMCBMeMBer，详细推导了目标势的后验期望(PENT)准则的高斯混合(GM)实现过程，以 GM-PENT 作为评价准则进行相应的传感器控制方法的研究。 Moreover, a detailed Gaussian Mixture (GM)implementation of the posterior expected number of tar-gets (PENT)criteria is given based on CK-GMCBMeMBerF, and the corresponding sensor control strategy is studied withGM-PENT as the evaluation criteria.
926 最后，仿真实验验证了所提算法的有效性。 Finally, simulation results verify the effectiveness of these proposed algorithms.
927 为了提高传感网中数据重构精度以及降低不可靠链路丢包对压缩感知(Compressive Sensing，CS)数据收集的影响，本文提出了一种基于压缩感知丢包匹配数据收集算法(Packet Loss Matching Data Gathering AlgorithmBased on Compressive Sensing，CS-MDGA)。 In order to improve the data reconstruction accuracy and alleviate the influence of packet loss over unrelia-ble links on the Compressive Sensing (CS)data gathering in sensor networks, we propose a Packet Loss Matching DataGathering Algorithm Based on Compressive Sensing (CS-MDGA)in this paper.
928 本文算法通过压缩感知技术构建了全网数据间的“关联效应”， This proposed algorithm establishes the correlation effect of the network data with the CS technique.
929 并设计了基于丢包匹配的稀疏观测矩阵(Sparse Observation Matrix Based on Packet Loss Matching，SPLM)，证明了该观测矩阵概率趋近于“1”时，满足的等距约束条件(Restricted Isometry Property，RIP)，完成了节点间多路径路由数据的可靠交付。 We further design the Sparse Observation Matrix based onPacket Loss Matching (SPLM )in this paper. In addition, we prove that the designed observation matrix satisfies the Re-stricted Isometry Property (RIP)with a probability arbitrarily close to 1, which can guarantee the reliable delivery of themulti-path routing data among different nodes.
930 仿真实验结果表明，本文算法在链路丢包率为 60% 情况下，相对重构误差仍小于 5% ， The simulation results show that the relative reconstruction error of this pro-posed algorithm is still lower than 5% even when the packet loss rate of the link is as high as 60% .
931 验证了本文算法不仅具有较高的重构精度，而且还可以有效缓解不可靠链路丢包对 CS 数据收集的影响。 Therefore, it is verifiedthat this proposed algorithm not only exhibits high reconstruction accuracy, but also effectively alleviates the influence ofpacket losses over unreliable links on the CS-based data collection.
932 提出了一种基于多元经验模态分解(Multivariate Empirical Mode Decomposition，MEMD)的多元多尺度熵(Multivarite Multiscale Entropy，MMSE)特征提取方法分析多模态信号，进行人体静态平衡能力评估。 A MMSE feature extraction method based on MEMD was proposed to analyze multi-modal signals and e-valuate the static balance ability of human body.