ID	原文	译文
16765	否则中继节点协作转发经截断后的对数似然比(LLR)。	Otherwise, it directly sendsthe truncated LLR to the destination.
16766	放大转发(AF)、译码转发(DF)、分段转发(PF)和ZF协作可看作DT-FZF协作的特殊情况。	In addition, the proposed DT-FZF scheme incorporates the Amplify-Forward(AF), Decode-Forward(DF), Piecewise-Forward(PF) and ZF schemes, all of which can be the specialcase of the proposed scheme.
16767	在三中继系统，误比特率(BER)为10–3时，相比ZF协作，所提协作可获得约0.8 dB的性能增益。	At a Bit Error Rate (BER) is of 10–3, the DT-FZF scheme outperforms the ZFscheme by approximately 0.8 dB in a three-relay system.
16768	波形优化可有效抑制干扰，显著改善雷达探测性能。	Waveform optimization can effectively suppress the interference, and improve significantly radarperformance.
16769	针对全极化雷达，考虑发射波形满足能量和相似性双重约束，以最大化信杂噪比为准则，对发射波形和接收滤波器进行联合优化。	With considering polarimetric radars as the object of study and to maximize the output Signal-to-Clutter plus Noise Ratio (SCNR) as the merit of figure, an optimization problem of joint transmit waveform and receive filter design under both the energy and similarity constraints is constructed.
16770	该文设计了一种波形和滤波器的迭代优化算法，该方法序贯提高输出信杂噪比。	Then, an optimization procedure for transmit signal and receive filter which improves sequentially the SCNR is exploited.
16771	算法的每一次迭代需要分别解决一个凸问题和隐凸问题，整个算法的计算量与迭代次数和接收滤波器长度分别呈线性和多项式关系。	Eachiteration of the algorithm requires the solution of both a convex and a hidden convex optimization problem,and the resulting computational complexity is linear with the number of iterations and polynomial with thereceive filter length.
16772	最后，通过仿真实验分析了算法的收敛性、优化波形的模糊度函数方面的性质，与其他算法进行了对比，结果表明：与现有方法相比，该文方法可实现信杂噪比的有效提升。	Finally, the convergence of the algorithm and the property of the optimized waveform inthe ambiguous domain are analyzed through numerical experiments. Results show that, compared to theexisting methods, the proposed approach improves significantly the SCNR.
16773	一类分类是一种将目标类样本和其他所有的非目标类样本区分开的分类方法。	In the problem of one-class classification, One-Class Classifier (OCC) tries to identify samples of aspecific class, called the target class, among samples of all other classes.
16774	传统的一类分类方法针对所有训练样本建立一个分类器，忽视了数据的内在结构，在样本分布复杂时，其分类性能会严重下降。	Traditional one-class classification methods design a classifier using all training samples and ignore the underlying structure of the data, thus their classification performance will be seriously degraded when dealing with complex distributed data.