ID	原文	译文
1893	在杂波纹理服从 Beta 分布的极化合成孔径雷达(Polarimetric Synthetic Aperture Radar，POLSAR)图像目标检测中，提出了一种基于多视极化白化滤波(Multilook Polarimetric Whitening Filter，MPWF)的恒虚警(Constant FalseAlarm Rate，CFAR)检测解析新方法。	A new constant false alarm rate (CFAR)detection analysis method based on multilook polarimetric whit-ening filter (MPWF)is proposed in polarimetric synthetic aperture radar (POLSAR)imagery when the clutter obeys Beta distributed texture hypothesis.
1894	首先，假设乘积模型中纹理变量服从 Beta 分布，推导得到 MPWF 检测量的概率密度函数(Probability Density Function，PDF)。	Firstly, the texture variable in the product model obeys Beta distribution is assumed, and theprobability density function (PDF)of MPWF output is derived.
1895	然后，对概率密度函数积分得到虚警概率关于检测门限的解析式，并设计相应的 CFAR 检测流程。	Then, the analytical formula of probability of false alarm(PFA)is obtained by integrating probability density function (PDF), and the corresponding CFAR process is designed.
1896	最后，提出了基于 MPWF 的对数累积量估计方法，对 Beta 分布纹理变量参数 u 和 v 进行估计。	Fi-nally, a log-cumulants estimation method based on MPWF is proposed to estimate the texture parameters u and v of Beta dis-tribution.
1897	通过实测数据验证了新方法的有效性。	The effectiveness of the new method is verified by simulation data and measured data.
1898	实验结果表明 Beta 分布对某些区域的极化 SAR 数据有更好的拟合效果，	Simulation results show that Beta distribution has better goodness of fit on some regions in POLSAR images, and the new method has better CFAR per-formance compared with the existing methods.
1899	同时新方法与已有方法相比具有更好的 CFAR 保持能力。	The embedded and measured data also show that the new method has betterdetection performances than the existing methods.
1900	本文针对最优贝叶斯网络的结构学习问题，在动态规划算法(Dynamic Programming，DP)的基础上，使用 IAMB 算法(Incremental Association Markov Blanket，IAMB)计算得到的马尔科夫毯对评分计算过程进行约束，减少了评分的计算次数，提出了基于马尔科夫毯约束的动态规划算法(Dynamic Programming Constrained with Markov Blan-ket，DPCMB)，	To solve the problem about structure learning of optimal Bayesian network, this paper proposes dynamicprogramming constrained with Markov blanket (DPCMB), which uses Markov blanket calculated by incremental association Markov blanket (IAMB)to reduce the number of scoring calculations in dynamic programming.
1901	研究了 IAMB 算法中重要性阈值对 DPCMB 算法的各项性能指标的影响，给出了调整阈值的合理建议。	We research on the effectof the significance value in IAMB on the performance indicators of DPCMB algorithm, and give reasonable suggestions foradjusting the significance value.
1902	实验结果表明，DPCMB 算法可以通过调整重要性阈值，使该算法的精度与 DP 算法相当，极大地减少了算法的运行时间、评分计算次数和所需存储空间。	Experimental results show that the DPCMB algorithm can adjust the significance value sothat the accuracy of the algorithm is comparable to that of the DP algorithm, and running time, score calculation times, andmemory requirements of the algorithm are greatly reduced.