| ID | 原文 | 译文 |
| 17175 | 再结合坐标下降法(CDA)将复杂的高维耦合参数估计方法简化为循环迭代的1维参数估计方法，有效降低字典维度和估计复杂度，并引入Hooke-Jeeves算法提高估计精度。 | This complex multi-variable optimization problem is divided into a set of single variable optimization problems by using the Coordinate DescentAlgorithm (CDA). The separation effectively reduces dictionary dimensions and estimation complexity.Moreover, the Hooke-Jeeves algorithm is introduced to enhance estimation accuracy in each single variableoptimization problem. |
| 17176 | 最后根据各个散射中心的参数估计结果对它们的结构和位置进行识别，对仿真数据的处理实验验证了该文方法的有效性。 | Consequently, the proposed estimator for scattering parameters is not only efficient, butalso accurate. The structure and location of each scattering center can be identified according to the parameterestimation results. Simulation results confirm the validity of the proposed method. |
| 17177 | 在复杂的浅海环境中，水声信道具有强烈的空变和时变特性，致使水声通信系统的鲁棒性很难得到保证。 | Water acoustic channel is severely affected by time-space variation, which destroys the robustness of underwater acoustic communication system. |
| 17178 | 该文不同于依赖复杂信道编码和信道均衡手段的传统水声通信算法，将流形学习思想应用于高维海洋环境参数空间刻画及信号空间映射中，为水下数据传输提出创新方案。 | By introducing manifold learning in the analysis ofhigh dimensional underwater environment and channel equalization processing, a novel underwater acousticcommunication algorithm is presented. |
| 17179 | 从声场角度出发，结合浅海实验数据，分析通信信号时空起伏特性，研究环境参数空间和声场信号空间的内在关系，提出了基于非线性流形学习算法增加合理的物理约束，结合信道稀疏特性，对于高维非线性水声信号系统的冗余维度信息进行维数约简，映射到稳定的低维目标空间，降低信道时空起伏对通信系统的影响。 | By establishing the mapping between environment parameter space andsignal space, several physical restrictions can be posed on non-linear manifold learning algorithm. Moreover, the sparse property can reduce the dimension of underwater acoustic channel in order to exclude high dimensional non-linear noise from channel time-space variation. |
| 17180 | 仿真和实验结果验证了算法的可靠性和有效性。 | Both sound field analysis and shallow water experimentaldata verify the validity and the robustness of the proposed algorithm. |
| 17181 | 针对组网雷达系统多目标跟踪场景，该文提出一种面向射频(RF)隐身的组网雷达射频辐射资源优化分配算法。 | In the scenario of multi-target tracking by a radar network system, a Radio Frequency (RF) stealth-based optimal RF resource allocation algorithm in radar network is proposed. |
| 17182 | 首先，采用目标跟踪误差的贝叶斯克拉美-罗下界(BCRLB)作为目标跟踪性能指标。 | Firstly, the Bayesian Cramer-RaoLower Bound (BCRLB) of target tracking error is used as the target tracking performance index. |
| 17183 | 其次，以各雷达照射目标的驻留时间资源和辐射功率资源加权和为优化目标，以BCRLB不大于给定目标跟踪精度阈值及系统射频辐射资源作为约束条件，建立了包含雷达节点分配方式、驻留时间和辐射功率3个优化变量的优化模型。 | Secondly, theoptimization model is established which includes three optimization variables: radar node selection, dwell timeand radiation power. In this model, the objective function is the weighted sum of the dwell time resources and radiation power resources of each radar, the constraint condition can be conclude that the BCRLB must be less than the given threshold and the system RF radiation resources must be between the upper and lower limits. |
| 17184 | 然后，采用两步分解法对上述优化模型进行了求解，即先固定雷达节点选择，利用内点法对简化后的非凸非线性优化模型进行求解，之后再通过匈牙利算法确定最佳雷达节点分配方式。 | Then, the two-step decomposition method is used to solve the above optimization model. The radar nodeselection is fixed first, then the interior point method is used to solve the simplified non-convex nonlinearoptimization model, and then the Hungarian algorithm is used to determine the best radar node selection mode. |