ID 原文 译文
21075 相干平面波复合(CPWC)成像算法采用多个角度平面波成像结果直接叠加的方式进行成像，具有速度快，质量高等优点，CPWC成像直接叠加的成像方式，忽略了平面波成像结果之间的相干性。 The Coherent Plane-Wave Compounding (CPWC) algorithm is based on the recombination of severalplane-waves with different steering angles, which can achieve high-quality images with high frame rate.However, CPWC ignores the coherence between the plane-wave imaging results.
21076 相干系数(CF)加权算法可以有效提高成像的分辨率和对比度，降低了背景成像质量。 Coherence Factor (CF)weighted algorithm can effectively improve the imaging contrast and resolution, while it degrades the background speckle quality.
21077 该文提出了短阶相干系数(SLCF)加权算法， A Short-Lag Coherence Factor (SLCF) algorithm for CPWC is proposed.
21078 该算法采用角度差异参数来确定相干系数的阶数，根据角度差异较小的平面波输出计算相干系数，对CPWC成像结果进行加权成像。 SLCFuses the angular difference parameter to ascertain the order of the coherence factor and calculates the coherence factor for the plane-waves with small angular difference. Then, SLCF is utilized to weight CPWC to obtain thefinal images.
21079 仿真和实验结果表明SLCF加权算法相对于传统的CPWC成像算法，可以改善成像的横向分辨率和对比度。 Simulated and experimental results show that SLCF-weighted algorithm can improve the imaging quality in terms of lateral resolution and Contrast Ratio (CR), compared with CPWC.
21080 相对CF和广义相干系数(GCF)算法，SLCF可以提高对比度和背景成像质量，而且运算量更低。 In addition, in comparison with CF and Generalized Coherence Factor (GCF) weighted algorithm, SLCF can achieve better background speckle quality and it has lower computational complexity.
21081 该文基于随机有限集的多目标滤波器提出一种基于目标威胁度评估的传感器控制策略。 This paper proposes a threat assessment based sensor control by using multi-target filter withrandom finite set.
21082 首先，在部分可观测马尔科夫决策过程(POMDP)的理论框架下，给出基于信息论的传感器控制一般方法。 First, the general sensor control approach based on information theory is presented in theframework of Partially Observable Markov Decision Process (POMDP).
21083 其次，结合目标运动态势对影响目标威胁度的因素进行分析。 Meanwhile, combined with targetmovement situation, the factors that affect the target threat degree are analyzed.
21084 然后，基于粒子多目标滤波器估计多目标状态，依据多目标运动态势的评估研究建立多目标威胁水平，并从多目标分布特性中深入分析并提取出当前时刻最大威胁度目标的分布特性。 Then, the multi-target state is estimated based on the particle multi-target filter, the multi-target threat level is established according to the multi-target motion situation, and the maximum threat target distribution characteristic is analyzed and extracted from the multi-target distribution characteristic.