ID	原文	译文
1623	本文结合遗传算法和 Otsu法对群距离-频率-能量三维电离图进行信号区域提取，通过连通域标记方法进行离散点噪声消除，获取“干净”信号，	In this paper combining genetic algorithm and Otsu for the signal extraction of the group-frequency-energy three-dimensional ionogram.
1624	然后利用 Canny 算子获得频率对应回波信号分布的最近和最远群距离，在群距离-频率-仰角三维电离图上，提取相应频率的仰角-群距离分布数据，分别利用线性曲线拟合、对仰角求对数拟合和对群距离求对数拟合的方式对数据点进行处理，	Using the connected domain marking to eliminate discrete pointnoise, obtain the clean signal, and use the Canny detetion method to obtain the nearest and farthest distance of each frequencyin the echo signal. On the group-frequency-elevation three dimensional ionogram, the elevation-group data of each frequencyis acquired, Fitting the points by the polynomial fitting, logarithmic fitting of the elevation and logarithmic fitting of thegroup,
1625	通过青岛观测站的测高仪数据对拟合后的曲线分别进行了误差分析，	Finally performing error analysis of the fitted curve through the vertical detection data of the Qingdao observation sta-tion.
1626	结果显示，对群距离求对数拟合得到的曲线与目标曲线的符合较好，证明了提取方法的有效性。	The result show that the logarithm of the group obtained curve is in good agreement with the target curve. The validity of the extraction method is proved.
1627	参数调节随机共振系统中参数的选择对输出信号的效果优劣具有决定性作用。	The selection of the parameters of the stochastic resonance system plays a decisive role in the effect of the output signal.
1628	本文针对目前随机共振无法通用地处理多类微弱通信信号的问题，提出基于自适应调参随机共振的信号增强方法。	In this paper, aiming at the problem that the stochastic resonance cannot process many kinds of weak commu-nication signals in general, an enhancement method based on adaptive parameter-tuning stochastic resonance is proposed.
1629	首先，从信号的特征子空间角度阐释了随机共振的能量转移本质，提出将基于奇异值分解的测度函数作为评价函数进行寻优。	Firstly, the energy transfer nature of the stochastic resonance is explained from the characteristic subspace of the signal, and ameasuring function based on singular value decomposition is presented as the evaluation function.
1630	其次，在分析了两个不同系统参数的作用后，利用幅度归一化对单参数优化，降低了复杂度，并将滑动平均滤波器加入随机共振模块来防止幅度漂移。	Secondly, after analyzingthe effects of two different system parameters, the amplitude normalization is utilized to optimize one single parameter, which reduces the complexity. And the sliding average filter is added to the module to prevent the phenomenon of the amplitude drift.
1631	最后，以人工鱼群算法为基础，模块化设计出方法的整体框架和具体步骤。	Finally, based on the artificial fish swarm algorithm, the overall framework and specific steps of the method are modu-larly designed.
1632	仿真结果表明，针对四类共九种信号，该方法能够以平均 4 至 5 次的迭代收敛速度实现带噪声的信号和非线性系统的匹配。	The simulation result demonstrates that the proposed method can match the noisy signal and the nonlinear sys-tem with an average of 4 to 5 iteration convergence speeds for four kinds of signals.