ID 原文 译文
18405 针对BISON算法迭代轮数异常高(一般为3n轮，n为数据分组长度)且密钥信息的异或操作由不平衡Bent函数决定的情况，该文采用了一类较小绝对值指标、高非线性度、较高代数次数的平衡布尔函数替换BISON算法中的Bent函数， Notice that the number of iterationrounds of BISON algorithm is rather high (It needs usually to iterate 3n rounds, n is the block length of data)and Bent function (unbalanced) is directly used to XOR with the secret key bits. In order to overcome theseshortcomings, a kind of balanced Boolean functions that has small absolute value indicator, high nonlinearityand high algebraic degree is selected to replace the Bent functions used in BISON algorithm.
18406 评估了新变体BISON算法抵御差分密码分析和线性密码分析的能力。 Moreover, the abilities of this new variant BISON algorithm against both the differential cryptanalysis and the linear cryptanalysis are estimated.
18407 研究结果表明：新的变体BISON算法仅需迭代n轮； It is shown that the new variant BISON algorithm only needs to iterate n-roundfunction operations;
18408 当n较大时(如n=128或256)，其抵御差分攻击和线性攻击的能力均接近理想值。 If n is relative large (e.g. n=128 or n=256), Its abilities against both the differential cryptanalysis and the linear cryptanalysis almost achieve ideal value.
18409 且其密钥信息的异或操作由平衡函数来决定，故具有更好的算法局部平衡性。 Furthermore, due to the balanced function is directly XORed with the secret key bits of the variant algorithm, it attains a better local balance indeed.
18410 在运动目标的无源定位场景下，闭式算法在低噪声情况下可以到达克拉美罗下界(CRLB)，但是这些算法往往不能适应较大的测量噪声环境。 In the passive location of moving target, the closed-form solution can reach Cramér-Rao LowerBound (CRLB) under the low noise level, but these algorithms often can not adapt to the large measurementnoise condition.
18411 针对目前闭式算法适应大噪声能力较差这一问题，该文联合到达时间差(TDOA)以及到达频率差(FDOA)，提出一种基于半定松弛(SDR)技术的无源定位算法。 For this problem, this paper proposes a passive positioning algorithm based on the Semi-Definite Relaxation (SDR) using Time Difference Of Arrival (TDOA) and Frequency Difference Of Arrival(FDOA).
18412 该算法首先构建传统闭式解的伪线性方程， Firstly, this method constructs the pseudo-linear equation of the typical closed-form solution.
18413 其次利用随机鲁棒最小二乘(SRLS)的思想以及目标参数与额外变量之间的非线性关系，将无源定位问题转化为了具有2次等式约束的最小二乘问题； Secondly, the idea of Stochastic Robust Least Squares (SRLS) and the nonlinear relationship between thetarget parameters and the additional variables are used to transform the localization problem into the leastsquares problem with quadratic equality.
18414 随后，将半定松弛技术应用到这一问题上，约束最小二乘问题松弛为半定规划(SDP)问题，最后，借助优化工具箱可以有效地对目标参数进行求解。 Using Semi-Definite Programming (SDP) technique, constrained leastsquares problem is then converted into the SDP problem, which is finally solved by the optimization toolbox.