ID 原文 译文
22615 无线带内全双工(IBFD)具有频谱效率倍增潜力，应用到多用户接入时，共享信道的优化分配将决定系统容量的实际增益。 The potential of wireless In-Band Full-Duplex (IBFD) in doubling spectral efficiency raises a special issue in channel allocation for Multi-user applications in capacity improvement.
22616 该文分析多用户争用冲突的干扰特性及链路分配的约束条件，以容量最大为优化目标，针对双向全双工(BFD)、全双工中继(FDR)以及两者混合的传输模式，建立信道资源分配的问题模型。 This paper presents an analysis on mutual interferences, contention conflicting and allocation constraints for different modes of full-duplex transmission. The maximization number of transmission links is chosen as the objective of multi-user system optimization, and a mathematic programming problem of IBFD channel allocation for three transmission modes, including Bidirectional Full-Duplex (BFD), Full-Duplex Relay (FDR) and the mixed of two is proposed.
22617 通过典型站点分布的最优解计算，分析 IBFD 系统相对半双工(HD)的容量增益。 The exact optimal solutions are researched for two kinds of typical topology to find the capacity improvement of IBFD to Half-Duplex (HD).
22618 结果表明，BFD 有理想的 100%增益，而 FDR 有较大变化范围(最低增益 25%)。 The results show that BFD multi-user system has an ideal capacity gain 100% in relative to HD, while FDR reveals a wide range of gain with 25% in the lowest.
22619 此外，计算发现，站点间传输链路呈现 1 维链型结构的容量增益明显优于 2 维平面分布的链路结构。 In addition, the gain of one dimensional chain of stations is found significantly superior to that of two-dimensional distribution.
22620 基于压缩感知(CS)理论的稀疏线性调频步进信号(SFCS)逆合成孔径雷达(ISAR)成像技术能够从少量观测数据中高概率重构出目标像，其中，观测矩阵的优化设计是提高成像质量和减少观测数据量的有效途径。然而，现有的观测矩阵优化设计研究通常没有考虑目标特征信息的有效利用，对目标的自适应能力不足。 The ISAR imaging technology with sparse Stepped-Frequency Chirp Signals (SFCS) based on Compressive Sensing (CS) theory can construct the target image from a few of measurements with high probability, where the measurement matrix optimization is an effective way of improving the imaging quality and reducing the measurements. However, most of the existing measurement matrix optimization methods do not utilize the target characteristic, which leads to low adaptive ability of target. Therefore, an adaptive measurement matrix optimization method for Inverse Synthetic Aperture Radar (ISAR) Imaging with sparse SFCS is proposed in this paper, where the actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix. However, most of the existing measurement matrix optimization methods do not utilize the target characteristic, which leads to low adaptive ability of target.
22621 因此，该文在充分利用目标特征信息的基础上，结合稀疏 SFCS 信号的实际物理观测过程，提出一种 ISAR 成像观测矩阵自适应优化方法。 Therefore, an adaptive measurement matrix optimization method for Inverse Synthetic Aperture Radar (ISAR) Imaging with sparse SFCS is proposed in this paper, where the actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix.
22622 该方法首先建立参数化稀疏表征成像模型以解决稀疏 SFCS 信号多普勒敏感问题，在此基础上，以在达到成像质量要求条件下使用最少观测数据量获得最优成像结果为目标对观测矩阵进行自适应优化设计，最终能够利用最少的数据量获得满意的目标成像结果。 In the method, a parametric sparse representation model of ISAR imaging is established to solve the Doppler sensitivity firstly. On the basis, the measurement matrix is optimized with the goal of obtaining the best target image with the minimum measurements under a given image quality requirement. As a result, the expected imaging results can be obtained with minimum measurements by using the optimized measurement matrix.
22623 仿真实验验证了该算法的有效性。 The effectiveness of the proposed method is demonstrated by experiments.
22624 在红外目标跟踪中，由于目标所处的背景信息复杂多变和目标外观的显著变化，单一的分类器不足以拟合多模态的数据。 In the infrared object tracking, the single classifier is not enough to fit the multimodal data due to the complex background information of the target and the significant change in the appearance.