| ID | 原文 | 译文 |
| 17085 | 通过引入代数图论实现监测空间内量测集合的划分，通过修正经典贝叶斯算法的似然比定义避免航迹的误丢弃。 | The clustering of measurement sets in surveillancevolume is achieved by introducing the algebraic graph theory. The rejection of true track is avoided by modifythe definition of classical Bayesian likelihood ratio. |
| 17086 | 实测数据处理结果证明该算法具备准确划分各个子群并快速起始各子群航迹的能力。 | Results from actual field tests demonstrate the capability ofclustering group targets precisely and promoting group tracks effectively. |
| 17087 | 针对时变水声信道估计和均衡问题，该文提出基于叠加训练序列(ST)和低复杂度频域Turbo均衡(LTE)的时变水声信道估计和均衡(ST-LTE)算法。 | To solve the problems of time-varying underwater acoustic channel estimation and equalization, anestimation and equalization algorithm of time-varying underwater acoustic channel based on SuperimposedTraining (ST) and Low-complexity Turbo Equalization (LTE) in frequency domain (ST-LTE) is proposed. |
| 17088 | 基于叠加训练序列方案，将训练序列和符号线性叠加，使得训练序列和符号信道信息一致；基于最小二乘算法，进行信道估计。 | Based on the ST scheme, the training sequence and symbols are linearly superimposed to make the channelinformation of the training sequence and symbols consistent; Based on the least square algorithm, channelestimation is performed. |
| 17089 | 基于频域训练序列干扰消除技术，在频域消除训练序列对符号的干扰； | Based on the interference elimination technique of training sequence in frequencydomain, the interference of training sequence on symbols is eliminated in frequency domain; |
| 17090 | 基于频域线性最小均方误差(LMMSE)均衡算法，通过先验、后验、外均值和方差的计算，实现低复杂度信道均衡(符号估计)； | Based on theLinear Minimum Mean Square Error (LMMSE) equalization algorithm in frequency domain, the low-complexitychannel equalization (symbol estimation) is realized by the calculation of prior, posterior, extrinsic mean andvariance; |
| 17091 | 基于Turbo均衡算法，软重构叠加训练序列和更新信道估计，进行均衡器和译码器的信息交换，利用编码冗余信息，大幅度提升信道均衡性能。 | Based on the Turbo equalization algorithm, soft reconstruction of superimposed training and updateof channel estimation are conducted, the information exchange between equalizer and decoder is also carried out and the performance of channel equalization is extremely improved by using coding redundancy information. |
| 17092 | 进行仿真、水池静态通信试验(通信频率12 kHz，带宽6 kHz，采样频率96 kHz，符号传输速率4.8 ksym/s，训练序列和符号的功率比为0.25:1)和胶州湾运动通信试验(通信频率12 kHz，带宽6 kHz，采样频率96 kHz，符号传输速率3 ksym/s，训练序列和符号的功率比为0.25:1)，仿真和试验结果验证了所提算法的有效性。 | Simulation, static communication experiment in a pool (communication frequency is 12 kHz, bandwidth 6 kHz,the sampling frequency 96 kHz, the transmission rate of symbols 4.8 ksym/s and the power ratio of the trainingsequence on symbols 0.25:1) and moving communication experiment in Jiaozhou Bay (communication frequencyis 12 kHz, bandwidth 6 kHz, the sampling frequency 96 kHz, the transmission rate of symbols 3 ksym/s and thepower ratio of the training sequence on symbols 0.25:1) are carried out and simulation and experimental resultsverify the effectiveness of the proposed algorithm. |
| 17093 | 逆合成孔径雷达(ISAR)目标回波具有明显的稀疏特征，传统的凸优化稀疏ISAR成像算法涉及繁琐的正则项系数调整，严重限制了超分辨成像的精度及便捷程度。 | Due to the echoes of the Inverse Synthetic Aperture Radar (ISAR) imagery are spatially sparse, the conventional convex optimization for the sparse image recovery involves tedious adjustment for the regularization parameter, which seriously limits the accuracy and the convenience of the image formation. |
| 17094 | 针对此问题，该文面向非约束Lasso正则化模型，建立分层贝叶斯概率模型，将非约束的范数正则化问题等效转化成稀疏拉普拉斯先验建模问题，并在分层贝叶斯Lasso模型中建立正则项系数依赖的概率分布，从而为实现完全自动化参数调整提供便利条件。 | In this paper, the unconstrained least absolute shrinkage and selection operator (Lasso) model is introduced for the regularization problem, and it is equivalently transformed into sparse Bayesian inference under theLaplacian prior. More specifically, a hierarchical Bayesian model is established. In such cases, multiple hyper-parameters with multi-level conditional probability distribution are introduced. Due to the equivalent transformation, the manual choice of the regularization parameter can be replaced by automatic determination under the hierarchical Bayesian model, which provides convenience of fully conditional probability adjustment. |