ID	原文	译文
2313	针对复杂战场环境下机动目标跟踪难题，提出一种认知雷达目标跟踪算法。	A cognitive radar target tracking algorithm is proposed for the tracking problem in complex battlefield envi-ronment.
2314	基于人类“感知-行动”循环思想，首先把目标径向距离、径向速度和方位等量测的克拉美罗下限近似为量测误差协方差，用信息熵描述目标跟踪的不确定性，	Based on the theory of human“perception-action”cycle, first, the Cramer-Rao lower bound (CRLB)of target ra-dial distance, radial velocity and azimuth is approximated to the measurement error covariance.
2315	然后以最小熵为准则建立了雷达接收端数据和发射端信号处理之间联系;	Then, the information entropyis used to describe the uncertainty of target tracking, and the connection between data processing in radar receiver and signal processing in radar transmitter is established with the criterion of minimum entropy.
2316	为避免传统交互式多模型(In-teracting Multiple Model，IMM)算法由于模型转移概率设置不合理所带来的跟踪精度下降问题，受人脑三阶段记忆机制启发，将“记忆”嵌入 IMM 算法，通过自适应调整模型转移概率，增强了优势模型的交互主导性，弱化了不匹配模型的不良竞争。	Furthermore, inspired by the three stagememory mechanism of human brain, “memory”is nested in Interacting Multiple Model (IMM)algorithm to overcome thetracking precision degradation problem when the model transition probability is set improperly. Thus, the transition probabili-ty can be adaptively adjusted to enhance the dominant model and weaken the bad competition of the mismatched model.
2317	仿真实验验证了算法的有效性。	The simulation results verify the effectiveness of the proposed algorithm.
2318	基于非负矩阵分解的语音去噪，在提高语音信号信噪比的同时，也会引起语音失真，从而导致噪声环境下说话人确认系统性能下降。	While nonnegative matrix factorization based speech enhancing methods can improve signal to noise ratio(SNR)of recovered speech signal, these methods lead to the speech distortion, and thus degrade the performance of speaker verification system under noisy environment.
2319	本文提出基于分区约束非负矩阵分解的语音去噪方法(Nonnegative Matrix Factorizationwith Partial Constrains，PCNMF)，目的是在未知和非平稳噪声条件下提高话人确认系统的鲁棒性。	This paper proposes a nonnegative matrix factorization with partial constrains(PCNMF), with objective of enhancing the robustness of speaker verification system in presence of unknown and unstable noises.
2320	PCNMF 在满足分区约束条件的基础上分别构建语音字典和噪声字典。	PCNMF constructs the speech and noise dictionaries while satisfying partition conditions.
2321	考虑到传统语音训练产生的语音字典往往含有一定的噪声成分，PCNMF 通过数学模型产生基音及泛音频谱，在此基础上利用该频谱模仿人声的共振峰结构来合成字典，从而保证语音字典纯净性。	Considering that the speechdictionary generated by traditional speech training contains a little noise element, PCNMF generates speech dictionary usingthe spectra of pitch and their harmonics via mathematical model, and accordingly imitates the formant structure of human voice. The purpose is to guarantee the purity of speech dictionary.
2322	另一方面，为了克服传统噪声字典构建方法带来的部分噪声信息丢失问题，PCNMF 对在线分离出的噪声样本进行分帧和短时傅里叶变换，然后以帧为单位线性组合生成噪声字典。	In addition, in order to alleviate the problem about the loss of the information of the noise sample, PCNMF performs framing operation and Short-Time Fourier Transform against the noise samples separated online, and then generates noise dictionary by means of linear combination of the spectrum frames of the noise samples.