ID 原文 译文
18385 模型失配误差还与平台航行参数有关，模型失配误差对速度变化敏感，随着航行速度增大，呈近似线性趋势增大，影响严重； The error space characteristics are approximate concentric ellipticdistribution; the model mismatch error is related to the navigation parameters of the platform. The modelmismatch error is sensitive to the speed change. As the navigation speed increases, the approximate linear trendincreases and the impact is serious.
18386 航向角对全局精度变化范围影响小，主要影响模型失配误差的空间分布，体现为一种随航向角“旋转”的特性，且椭圆横轴方向与平台运动方向趋于一致。 The heading angle has little influence on the global precision variationrange, which mainly affects the space of the model mismatch error. The distribution is embodied as a kind of“rotation” with the heading angle, and the direction of the ellipse is aligned with the direction of motion of theplatform.
18387 为了解决多任务观测条件下时域流信号动态重构面临的块效应问题，该文基于重叠正交变换(LOT)和稀疏贝叶斯学习的贪婪重构框架先后提出了一种流信号多任务稀疏贝叶斯学习算法及其鲁棒增强型的改进算法， To eliminate the blocking effects in the dynamic recovery of the streaming signals observed frommultiple tasks in time domain, a streaming multi-task sparse Bayesian learning based algorithm and its robustenhanced version are proposed in this paper,
18388 前者将LOT时域滑窗推广到多任务条件下，通过贝叶斯概率建模将未知的噪声精度的估计任务从信号重构中解耦并省略，后者进一步引入了重构不确定性的度量，提高了算法的鲁棒性和抑制误差积累的能力。 where the former extends Lapped Orthogonal Transform (LOT)sliding window in time domain to multi-task condition, and decouples the estimation of unknown noiseaccuracy from signal reconstruction by Bayesian probability modeling and omits it, the latter further introducesthe measurement of reconstructed uncertainty, which improves the robustness of the algorithm and the abilityto suppress the accumulation of errors.
18389 基于浮标实测数据的实验结果表明，相比多任务重构领域代表性较强的时间多稀疏贝叶斯学习(TMSBL)和多任务压缩感知(MT-CS)算法，本文算法在不同信噪比、观测数目和任务数目条件下具有显著更高的重构精度、成功率和效率。 Experimental results based on measured meteorological data shows thatthe proposed algorithms have significantly higher reconstruction accuracy, success rate and running speed thanthe representative algorithms in the field of compressed sensing from multiple measurement vectors, namely,the Temporal Multiple Sparse Bayesian Learning (TMSBL) algorithm and the Multi-Task-Compressed Sensing(MT-CS) algorithm, under different conditions of Signal-to-Noise Ratios, number of observations and tasks.
18390 相参雷达系统下的非相干积累检测方法，可以提高雷达的目标检测速度，达到实时处理的要求。 The non-coherent integration detectors for coherent radar systems can promote the detection rate ofthe radar and meet the required real-time processing.
18391 然而，相参雷达系统下的非相干积累检测方法对参考单元数、脉冲积累数、杂波散斑协方差矩阵以及海杂波模型的形状参数均是非恒虚警(CFAR)的。 However, these detectors are not Constant False AlarmRate (CFAR) with respect to the reference cell number, the accumulated pulse number, the clutter specklecovariance matrix, and the shape parameter of the sea clutter model.
18392 该文基于块白化的海杂波预白化方法，提出预白化单元平均恒虚警(PWCA-CFAR)检测方法和预白化单元中值恒虚警(PWCM-CFAR)检测方法，并使用了匹配于参考单元数、脉冲积累数、形状参数的自适应门限，确保提出检测方法的恒虚警特性。 Based on block-whitening method towhiten the sea clutter, a Pre-Whitening Cell-Averaging CFAR (PWCA-CFAR) detector and a Pre-WhiteningCell-Median CFAR (PWCM-CFAR) detector are proposed where the detection thresholds matching thereference cell number, accumulated pulse number and shape parameter are used.
18393 实验结果表明，当存在异常单元时，PWCM-CFAR检测方法的检测性能优于PWCA-CFAR检测方法。 The experiment results showthat the PWCM-CFAR detector attains better detection performance than the PWCA-CFAR detector whenthere exist abnormal cells.
18394 针对现有相似实体搜索方法缺乏对于观测序列长度的自适应性，且搜索过程数据存储开销过大，搜索结果准确性较低的问题，该文提出相似度自适应估计的物联网实体高效搜索方法(SAEES)。 The existing similar entity search method has poor adaptability to the length of the observedsequence, and the data storage overhead in the search process is too large, and the accuracy of the search resultis insufficient. To this end, an efficient search method is proposed for the IoT Entity Search with SimilarityAdaptive Estimation (SAEES).