ID 原文 译文
20685 在休眠期间切换到低分辨率，需要唤醒时先通过UKF获得高分辨率计时开始时间的最优估计，再通过分辨率渐变的定时器中断的线性组合来进入高分辨率计时。 Being at a low frequency during sleep period, if a node needs to switch to wake-up period, it will first obtain the optimal estimation of the start time of high resolution timing period by UKF, then enter the high resolution timing period after a linear combination of a group of gradual-changing resolution timer IRQ.
20686 对Tmote平台的ContikiMAC协议进行的仿真实验中，在无线电占空比(RDC)为0.53%的情况下，所提方法比原始协议总能耗下降28.85%。 The simulations of ContikiMACprotocol on the Tmote platform are conducted. When the Radio Duty Cycle (RDC) is 0.53%, the proposed method reduces the total power consumption by 28.85% compared to the original protocol.
20687 针对多跳频信号空域参数估计问题，该文在稀疏贝叶斯学习(SBL)的基础上，利用跳频信号的空域稀疏性实现了波达方向(DOA)的估计。 To solve the problem of spatial parameter estimation of multi-frequency hopping signals, the sparsity in spatial domain of frequency hopping signals is used to realize the Direction Of Arrival (DOA) estimation based on Sparse Bayesian Learning (SBL).
20688 首先构造空域离散网格，将实际DOA与网格点之间的偏移量建模进离散网格中，建立多跳频信号均匀线阵接收数据模型； First, the spatial discrete grid is constructed and the offset betweenthe actual DOA and the grid points is modeled into it. The data model of the uniform linear array withmultiple frequency hopping signals is established.
20689 然后通过SBL理论得到行稀疏信号矩阵的后验概率分布，用超参数控制偏移量和信号矩阵的行稀疏程度； Then the posterior probability distribution of the sparse signal matrix is obtained by the SBL theory, and the line sparsity of the signal matrix and the offset is controlled by the hyper parameters.
20690 最后利用期望最大化(EM)算法对超参数进行迭代，得到信号矩阵的最大后验估计以完成DOA的估计。 Finally, The expectation maximization algorithm is used to iterate the hyper parameters, and the maximum posteriori estimation of the signal matrix is obtained to complete the DOA estimation.
20691 理论分析与仿真实验表明该方法具有良好的估计性能并能适应较少快拍数的情况。 Theoretical analysis and simulation experiments show that this method has good estimation performance and can adapt to less snapshots.
20692 在单样本(SMV)、低信噪比条件下，稀疏重构方法可提升时延估计精度，但现有的重构算法在支撑集元素的选择中存在错选和漏选的情况，从而导致估计精度受限。 Under Single Measurement Vector (SMV) and low Signal-to-Noise Ratio (SNR) conditions, the sparse reconstruction method can improve the estimation accuracy of Time Of Arrival (TOA). However, the existing reconstruction algorithms have some mistakes and missing in the selection of sparse support set elements, which leads to limited estimation accuracy.
20693 针对上述问题，该文提出一种基于循环匹配追踪(LMP)的稀疏重构时延估计算法。该方法引入了“循环删除，匹配添加”的思想，有效提升了直达径的估计精度。 In order to solve this problem, this paper proposes an algorithm based onsparse reconstruction Loop Matching Pursuit (LMP), which improves the estimation accuracy of the directpath.
20694 算法首先建立信道冲激响应稀疏表示模型； The algorithm first establishes a sparse representation model of channel impulse response.