ID 原文 译文
18765 最后通过现有的解模糊方法得到高精度且无模糊的DOA和极化参数估计值。 Finally, the estimates of Direction-Of-Arrival (DOA) and polarization parameter can be achieved by the existing disambiguate method.
18766 该文所提阵列不存在共心结构，相对于现有的含有共心式矢量传感器结构的阵列，大大降低了互耦影响，且可在不增加天线数目的前提下，有效扩展阵列的2维孔径，大大提高DOA估计精度。 Compared to the existing polarization sensitive array consists of collocatedvector sensor, the proposed one has no collocated configuration, which can reduce the mutual coupling effect.Additionally, the proposed method can also extend the spatial aperture and refine the direction-findingaccuracy without adding any redundant antennas.
18767 仿真结果证明该文所提方法的有效性。 Simulations are carried out to verify the effectiveness of the proposed method.
18768 为了提高强非线性信号的噪声消除和信道均衡能力，在核学习自适应滤波方法的基础上，该文提出一种基于惊奇准则的多尺度核学习仿射投影滤波方法(SC-MKAPA)。 In order to improve the ability of noise elimination and channel equalization of strong non-linearsignals, a Multi-scale Kernels learning Affine Projection filtering Algorithm based on Surprise Criterion (SC-MKAPA) is proposed on the basis of kernel learning adaptive filtering method.
18769 在核仿射投影滤波算法的基础上，对核组合函数结构进行改进，将多个不同高斯核带宽作为可变参数，与加权系数共同参与滤波器的更新； Based on the kernel affineprojection filtering algorithm, the structure of the kernel combination function is improved, and the bandwidthsof several different Gaussian kernels are taken as variable parameters to participate in the update of the filtertogether with the weighted coefficients.
18770 利用惊奇准则将计算结果稀疏化，根据仿射投影算法的约束条件对惊奇测度进行改进，简化其方差项，降低了计算的复杂度。 The calculation results are sparsed by using the surprise criterion, and the surprise measure is improved according to the constraints of the affine projection algorithm, which simplifies the variance term and reduces the calculation complexity.
18771 将该算法应用于噪声消除、信道均衡以及MG时间序列预测中，与多种自适应滤波算法及核学习自适应滤波算法进行仿真结果的对比分析，验证了该算法的优越性。 The algorithm is applied to noisecancellation, channel equalization, and Mackey Glass (MG) time series prediction. The simulation results are compared with the traditional adaptive filtering algorithm and the kernel learning adaptive filtering algorithm, it proves the superiority of the proposed algorithm.
18772 针对移动机器人导航过程中无法规避大型凹型障碍物问题，该文提出一种多状态的组合导航算法。 For the problem that mobile robot can not avoid large concave obstacles during navigation, this paper proposes a multi-state integrated navigation algorithm.
18773 算法按照不同的运动环境，将移动机器人的运行状态分类为运行态、切换态、避障态，同时定义了基于移动机器人运行速度和运行时间的状态双切换条件。 The algorithm classifies the running state of mobile robot into running state, switching state and obstacle avoidance state according to different moving environment, and defines the state double switching conditions based on the running speed and running time ofthe mobile robot.
18774 当移动机器人处于运行态时，采用人工势场法(APFM)进行导航，并实时观测毗邻障碍物的几何构型。 The Artificial Potential Field Method (APFM) is used to navigate and observe the geometric configuration of adjacent obstacles in real time.