ID 原文 译文
19715 该算法对iBeacon锚点与定位目标的距离进行解算，利用加速度计和陀螺仪的数据实现姿态阵和位置解算。 The new algorithm solves the distance between the iBeacon anchor and the locating target. The solution of attitude matrix and position areobtained respectively by using accelerometer and gyroscope data.
19716 将蓝牙锚点位置向量、载体速度误差信息等组成状态量，将惯性导航定位信息和蓝牙定位距离信息等组成观测量，设计无迹卡尔曼滤波器，实现iBeacon/MEMS-INS数据融合定位。 Bluetooth anchor position vector, the carrier speed error and other information constitute state variables. Inertial navigation location and bluetooth system distance information constitute measure variables. Based on state variables and measure variables, the UKF is designed to realize iBencon/MEMS-INS data fusion indoor positioning.
19717 实验测试结果表明，该算法有效解决MEMS-INS存在较大积累误差及iBeacon指纹定位存在跳变误差的问题，可以实现1.5 m内的定位精度。 The experimental results show that the proposed algorithm can effectively solve the problem of the large accumulation error of INS and the jump error of iBeacon fingerprint positioning, and this algorithm can realize 1.5 m positioning accuracy.
19718 为了提高行人再识别算法的识别效果，该文提出一种基于注意力模型的行人属性分级识别神经网络模型，相对于现有算法，该模型有以下3大优点：一是在网络的特征提取部分，设计用于识别行人属性的注意力模型，提取行人属性信息和显著性程度； In order to improve the accuracy rate of person re-identification, a pedestrian attribute hierarchy recognition neural network is proposed based on attention model. Compared with the existing algorithms, the model has the following three advantages. Firstly, the attention model is used in this paper to identify the pedestrian attributes, and to extract of pedestrian attribute information and degree of significance.
19719 二是在网络的特征识别部分，针对行人属性的显著性程度和包含的信息量大小，利用注意力模型对属性进行分级识别； Secondly,the attention model in used in this paper to classify the attributes according to the significance of thepedestrian attributes and the amount of information contained.
19720 三是分析属性之间的相关性，根据上一级的识别结果，调整下一级的识别策略，从而提高小目标属性的识别准确率，进而提高行人再识别的准确率。 Thirdly, this paper analyzes the correlationbetween attributes, and adjusts the next level identification strategy according to the recognition results of theupper level. It can improve the recognition accuracy of small target attributes, and the accuracy of pedestrian recognition is improved.
19721 实验结果表明，该文提出的模型相较于现有方法，有效提高了行人再识别的首位准确率，其中，Market1501数据集上，首位准确率达到了93.1%，在DukeMTMC数据集上，首位准确率达到了81.7%。 The experimental results show that the proposed model can effectively improve thefirst accuracy rate (rank-1) of person re-identification compared with the existing methods. On the Market1501dataset, the first accuracy rate is 93.1%, and the first accuracy rate is 81.7% on the DukeMTMC dataset.
19722 距离旁瓣可能导致强目标掩盖弱目标以及大量虚假目标的出现，针对认知雷达旁瓣抑制问题，该文提出一种基于序列优化的方法。 Range sidelobes may lead to weak targets masked by strong targets and false alarm. This paper proposes a sequential optimization method for the sidelobe suppression of cognitive radar.
19723 首先，将待测区域按距离单元进行划分，之后，基于最小均方误差准则，针对某距离单元进行发射-接收联合优化，所优化结果用于该距离单元散射点雷达截面积(RCS)的估计。 First, the region todetect is divided according to range cell. Second, the transmit waveform and receive filter are optimized jointly based on the principle of minimum mean square error against one range cell. The optimized transmit and receive systems are used in Radar Cross Section (RCS) estimation for the scatter in the current range cell.
19724 上述过程在场景内各距离单元序贯进行，并将已获估计值用于后续距离单元距离旁瓣的抑制，各散射点RCS值依次以递归方式获得，并循环更新。 The above process is carried out in each range cell in the scene sequentially. The acquired RCS estimate is used inthe sidelobe suprresion for the following range cells. The RCS estimation for all the range cells in the scene is obtained in a bootstrapping way successively and updated circularly.