ID 原文 译文
19535 其次，在粒子滤波的重要性权值计算的过程中，引入动态加权因子，采用传感器节点的测量值与目标状态之间的互信息熵，来反映传感器节点提供的目标信息量，从而获得各个节点相应的加权因子。 Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node.
19536 最后，采用3维场景下的簇-树型网络拓扑结构，跟踪监测区域内的目标。 Finally, the improved cluster-tree network topology is used to track the target inthree-dimensional space.
19537 实验结果显示，该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度，降低跟踪误差。 Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.
19538 针对非线性卫星信道，该文提出了两种基于回声状态网络(ESN)的在线盲均衡算法。 Two online blind equalization algorithms based on Echo State Network (ESN) in this paper are proposed for the nonlinear satellite channel.
19539 利用ESN良好的非线性逼近能力，将发送信号的高阶统计量(HOS)代入ESN，结合常模算法(CMA)和多模算法(MMA)构造盲均衡的代价函数，并采用递归最小二乘(RLS)算法对ESN输出权值进行迭代寻优，实现了Volterra卫星信道下常模和多模信号的在线盲均衡。 These two algorithms take advantage of the good nonlinear approximation of ESN to bring the High-Order Statistics (HOS) of the transmitted signal into the ESN, and constructing cost function of blind equalization by combining Constant Modulus Algorithm (CMA) and Multi-Modulus Algorithm (MMA). Then, the Recursive Least Squares (RLS) algorithm is used to iteratively optimizethe network output weights, and the online blind equalization of the constant modulus signals and the multi-modulus signals over the channel of Volterra satellite are realized.
19540 实验表明，该文算法可以有效降低非线性信道对发送信号产生的畸变，相较于传统的Vol-terra滤波方法，有更快的收敛速度和更低的均方误差值。 Experiments show that the proposed algorithms can effectively reduce the distortion of the transmitted signal by the nonlinear channel. Compared with the traditional Volterra filtering method, they have faster convergence speed and lower mean square error.
19541 针对残缺电磁矢量传感器的极化敏感阵列多参数联合估计问题，该文提出一种基于正交偶极子的均匀线阵的2维波达方向(Direction-Of-Arrival, DOA)估计算法。 To solve the problem that polarization sensitive array of defective electromagnetic vector sensorestimate multi parameter, a two-dimensional DOA estimation algorithm based on orthogonal dipole is proposedin this paper.
19542 首先，对极化敏感阵列的接收数据矢量的协方差矩阵进行特征分解，然后将信号子空间划分成4个子阵，根据旋转不变子空间(ESPRIT)算法分别求出其中1个子阵与其它3个子阵的相位差，再对不同子阵间的相位差进行配对，最后根据相位差求出信号的DOA估计和极化参数。 First, eigen decomposition of the covariance matrix is produced by the received data vectors of the polarization sensitive array. Then the signal subspace is divided into four subarrays, and the phase difference between one of the subarray and the others is obtained according to the ESPRIT algorithm. Then the phase difference between different subarrays is paired. Finally, the DOA estimation and polarization parameters ofthe signal are calculated according to the phase difference.
19543 由正交偶极子组成的均匀线阵使用极化MUSIC算法和传统ESPRIT算法无法进行2维DOA估计，该文提出的算法解决了这个问题，并且相较于极化MUISC算法降低了算法的复杂度。 The uniform linear array composed by orthogonaldipoles can not be two-dimensional DOA estimated by using the MUSIC algorithm and the traditional ESPRITalgorithm. The algorithm proposed in this paper solves this problem, and compared with the polarizationMUISC algorithm greatly reduces the complexity of the algorithm.
19544 仿真结果验证了该文算法的有效性。 The simulation results verify theeffectiveness of the proposed algorithm.