ID 原文 译文
24145 该文的设计对于微带反射阵列天线实现双频双极化性能具有重要的参考价值。 The design is valuable to other reflectarray in achieving dual-band dual-polarization performance.
24146 针对压缩感知检测方法不能准确测量伪随机动态测试信号电能量值的问题，该文首先分析了动态测试信号的频域稀疏性，证明动态测试信号满足压缩感知检测的条件； Focusing on the problem that Compressive Sensing (CS) measurement can not measure the electrical energy of pseudo random dynamic test signal accurately, in this paper, the spectral sparseness of pseudo random dynamic test signal is firstly analyzed, and dynamic test signal satisfies the measurement condition of compressive sensing is proved.
24147 然后采用系统稳态优化的方法，构造了一种确定型压缩感知测量矩阵，证明其符合限制等距特性(RIP)条件，最后提出了一种新型压缩感知动态测试信号电能量的测量方法。 Secondly, the system stable state optimization method is used to establish the deterministic compressive sensing measurement matrix, which meets the RIP (Restricted Isometry Property) condition. Finally, a new compressive sensing measurement methed for the electrical energy of pseudo random dynamic electric energy is proposed.
24148 实验结果表明：该文压缩感知测量方法的理论相对误差优于传统的采样功率电能测量方法，能够实现 m 序列动态测试信号的电能量值准确测量。 The experimental result shows that the theoretical error of the compressive sensing measurement is superior over traditional sample power electrical energy measurement, and it can measure the pseudo random dynamic electrical energy accurately.
24149 交互式多模型贝努利粒子滤波器(Interacting Multiple Model Bernoulli Particle Filter, IMMBPF)适用于杂波环境下的机动目标跟踪。 Interacting Multiple Model Bernoulli Particle Filter (IMMBPF) is suitable for maneuvering target tracking under cluttered environment.
24150 但是 IMMBPF 将模型信息引入粒子采样过程中会导致用于逼近当前时刻真实状态与模型的粒子数减少，而且每次递推各模型间的粒子都要进行交互，存在计算量过大的缺点。 However, when model information is introduced into particle sampling process in IMMBPF, it will lead to the number decline of particles which are applied to approaching the real state and model, and the computation load is heavy because of the interacting stage of particles in the recursion.
24151 为提升 IMMBPF 中单个采样粒子对于真实目标状态和模型逼近的有效性，该文提出一种改进的多模型贝努利粒子滤波器(Multiple Model Bernoulli Particle Filter, MMBPF)。 An enhanced Multiple Model Bernoulli Particle Filter (MMBPF) is proposed to improve the effectiveness of single particle to approximate the real target state and model.
24152 预先选定每一个模型的粒子数，且模型间的粒子不需要进行交互，减少了计算负荷。模型概率由模型似然函数计算得到，在不改变模型的马尔科夫性质的条件下避免了小概率模型的粒子退化现象。 The number of particles of each model is given in advance, and the posterior probability of each model is updated with the associate likelihood function, which avoids particle degeneracy without distorting the Markov property.
24153 仿真实验结果表明，所提出的 MMBPF 与 IMMBPF 相比，用较少的粒子数就可获得更优的跟踪性能。 Simulation results show that the proposed MMBPF achieves better tracking performance with fewer particles than IMMBPF.
24154 该文首次提出两种典型的移动散射体存在的车辆对车辆(V2V)的无线传播信道模型，一种是基于一次散射发射(SBT)和一次散射接收(SBR)的信道，另一种是基于两次散射(DB)的信道，并在这两种模型的基础上给出了同时包含一次散射发射，一次散射接收，两次散射和视距(LOS)分量的信道模型。 Two typical Vehicle-To-Vehicle (V2V) propagation channel models are proposed for the first time. One is that the channel is composed by Single-Bounce Transmit (SBT) and Single-Bounce Receive (SBR)components. The other is that the channel is composed by Double-Bounce (DB) components. Based on the two models, another model consisting of SBT, SBR, DB, and LOS components is proposed.