| ID | 原文 | 译文 |
| 1483 | 首次给出了 SPECK32 算法 10 条 6 轮零相关区分器和 SPECK48 算法 15 条 6 轮零相关区分器; | For SPECK, we first find 10 6-round zero-correlation linear approximations ofSPECK32 and 15 6-round zero-correlation linear approximations of SPECK48. |
| 1484 | 在较短的时间内，给出了 HIGHT 算法 17 轮的不可能差分和零相关区分器。 | Besides, we find 4 17-round impossible dif-ferentials and zero-correlation linear approximations of HIGHT in a few minutes. |
| 1485 | 与现有结果相比，无论是区分器的条数，还是搜索区分器的时间均有明显的提升。 | Compared with the existing results, boththe number and the search time of distinguishers are significantly improved. |
| 1486 | 此外，通过重新封装求解器 STP 的输出接口，建立了自动化的 SAT \SMT 分析模型，能够给出 ARX 算法在特殊输入输出差分和掩码集合下，不可能差分和零相关区分器轮数的上界。 | In addition, by repackaging the output interface of the STP solver, we establish the automated SAT \SMT model, which can give the upper bound of rounds of impossibledifferentials and zero-correlation linear approximations under special input and output differential and mask sets for ARXalgorithm. |
| 1487 | 研究了低信噪比时双基地 MIMO 雷达目标跟踪问题，提出了一种基于改进 AAJD(Adaptive AsymmetricJoint Diagonalization)的目标跟踪算法。 | The target tracking problem of bistatic MIMO radar under low SNR is studied, and a target tracking algo-rithm based on the improved AAJD(Adaptive Asymmetric Joint Diagonalization)is proposed. |
| 1488 | 首先，对 AAJD 算法进行改进，得到与特征值作用相同的变量，从而找出大特征值变量对应的特征矢量，解决了低信噪比时 AAJD 算法信号子空间扩展问题。 | Firstly, the AAJD algorithm isimproved to obtain the variable as the eigenvalue and the criterion of selecting the feature vector. The eigenvalue variablesare used to find the eigenvectors corresponding to the large eigenvalue variables. And the problem of signal subspace expan-sion in AAJD algorithm is solved at low SNR. |
| 1489 | 其次，在非稳定跟踪状态时消除特征值变量误差积累的影响，得到更加准确的信号子空间，并对 ESPRIT 算法进行改进，实现收发角度的配对和相邻时刻角度的自动关联。 | Secondly, the influence of the accumulation of the eigenvalue variables erroris eliminated in the unsteady tracking state. The obtained signal subspace is more accurate. Since the estimated eigenvectorsorder is random at each time, the ESPRIT algorithm is improved to achieve the automatic pairing of transceiver angle of thesame moment and the automatic association of the angle of the adjacent moment. |
| 1490 | 仿真结果表明改进 AAJD 算法低信噪比时能够实现角度跟踪，且收敛速度和稳定性能明显优于 AAJD算法。 | The simulation results show that the im-proved AAJD algorithm can realize the angle tracking with low signal to noise ratio, and the convergence speed and stabilityperformance are significantly better than AAJD algorithm. |
| 1491 | 针对在初始先验信息缺失时磁性目标滤波跟踪方法发散问题进行研究，本文提出了一种多初值模型的解决框架， | In order to solve the divergence of the magnetic target filter tracking method when the initial prior informa-tion is missing, this paper proposes a solution framework of multiple initial value models. |
| 1492 | 并以平方根形式的中心差分卡尔曼滤波器(Square-Root Central Difference Kalman Filter，SRCDKF)为例，结合多初值模型得到了 SRCDKF 自适应磁性目标跟踪算法。 | Taking the square-root centraldifference Kalman filter(SRCDKF)as an example, the SRCDKF adaptive magnetic target tracking algorithm is obtained bycombining multiple initial value models. |