ID 原文 译文
13614 在电磁探头下方5mm处的测试线圈上可测得1 600mV的感应电动势, 并可利用该感应电动势来对芯片引入故障。 The induction electromotive force of 1 600 mV can be measured on the measurement coil at 5 mm below the electromagnetic probe, and the fault can be induced to the chip by using this induction electromotive force.
13615 针对多星定位系统对地面静态目标的无源定位误差分析问题, 运用Fisher信息矩阵、Taylor级数、矩阵理论和统计理论, 综合考虑时差、频差、卫星位置误差以及卫星速度误差, 推导了到达时间差 (time difference of arrival, TDOA) /到达频率差 (frequency difference of arrival, FDOA) 联合定位误差克拉美·罗界 (Cramer-Rao lower bound, CRLB) 的简单表达式, 以及三星单独TDOA定位误差的CRLB, 进而给出了避免TDOA定位盲区的良好卫星构型设计的充分条件。 The passive location error and satellite configuration analysis of a static target on the Earth surface for passive multi-satellite localization systems is investigated.By using Fisher information matrix, Taylor series, matrix theory and statistical theory, a concise Cramer-Rao lower bound (CRLB) expression of time difference of arrival (TDOA) and frequency difference of arrival (FDOA) joint localization is derived, in which the TDOA measurement error, FDOA measurement error, satellite position error and satellite velocity error are all considered.Then, we derive the CRLB for three satellites location system based on TDOA. Furthermore, the sufficient condition of designing agood satellite configuration to eliminate the blind zones of TDOA location is given and proved.
13616 理论分析与仿真结果表明：在单独TDOA定位场景下良好的构型能完全消除定位盲区, 定位精度随主星-星下点连线与主星-副星连线的夹角逼近90°而逐渐提高。 Theoretical analysis and simulation results show that the blind zones of sole TDOA localization can be completely eliminated by employing agood satellite configuration, and the location precision is gradually improved with the intersection angle between the line of the primary satellite to the subastral point and the line of the primary satellite to the secondary satellite tending to 90 degree.
13617 通过引入FDOA与TDOA联合定位也能有效避免定位盲区, 提高定位精度。 Moreover, by introducing FDOA measurement, TDOA and FDOA joint localization can avoid the blind zones effectively and improve the positioning accuracy.
13618 针对宽带多极化雷达, 提出将高分辨一维距离像 (high resolution range profile, HRRP) 与极化信息相结合的算法, 获得目标在4种极化组态下的一维距离像并将其组成极化距离矩阵。 For broadband multi-polarization radar, this paper proposes an algorithm that combines high resolution range profile (HRRP) and polarization information to obtain the HRRP of target under four polarization configurations which is composed of the polarization distance matrix.
13619 该算法对目标进行全方位的特征抽取与建模, 以适应不同的姿态, 有助于减少高分辨一维距离像方位敏感性带来的影响。 This algorithm performs full-scale feature extraction and modeling of the target to adapt to different poses and effectively reduces the impact of HRRP azimuth sensitivity.
13620 然后提出了直接基于极化距离矩阵、Pauli分解和Freeman分解三种特征提取方式对极化距离矩阵进行目标特征的提取, 并将获得的目标特征向量结合起来送入搭建的深度卷积神经网络进行训练学习。 Then, distance matrix, Pauli decomposition and Freeman decomposition are used to extract the target features of the polarization distance matrix, and the obtained target feature vectors are combined and sent to the constructed deep convolutional neural network for training and learning.
13621 该方法不仅结合了不同的特征提取方式以对极化距离矩阵进行更全面的特征提取, 而且深度卷积神经网络的运用又对目标特征向量进行了深层学习, 仿真结果验证了该方法的有效性。 This method not only combines different feature extraction methods to extract more comprehensive features of the polarization distance matrix, but also deeply learns the target eigenvectors by using the deep convolution neural network.The simulation results verify the effectiveness of the proposed method.
13622 针对压缩感知框架下无设备目标定位 (device-free localization, DFL) 的字典失配问题, 提出一种基于链路选择学习 (link selection learning, LSL) 算法的DFL方式。 In order to overcome the problem of dictionary mismatch in the compressive sensing based device-free localization (DFL) , a link selection learning algorithm (LSL) is proposed to enhance the DFL performance.
13623 由于传统基于阴影模型的字典无法准确表达接收信号强度 (received signal strength, RSS) 变化与目标位置间的对应关系, 本文算法首先在训练阶段通过字典学习的方式更新初始字典。 Because the traditional shadowing-based dictionary cannot correctly describe the relationship between the received signal strength (RSS) and the target position, our algorithm first utilizes the dictionary learning (DL) technique to update the dictionary in the training phase, and uses the updated dictionary as the weight in the subsequent positioning stage.