ID 原文 译文
1043 针对非高斯背景下局部最优检测( Locally Optimal Detector，LOD) 结构复杂、稳健性弱的问题，对传统的限幅器进行改进，提出了一种自适应限幅检测器( Adaptive Limiter Detector，ALMD) 。 Aiming at the problem that the local optimal detector( LOD) has complex detection structure and poor ro-bustness for signal detection in Non-Gaussian noise background, an adaptive limiter detector ( ALMD) is proposed based on the improvement of traditional limiter.
1044 首先对弱信号的检测性能进行了系统研究， Firstly, the detection performance of weak signal is systematically studied.
1045 然后依据混合高斯模型对非高斯背景建模， Then thenon-Gaussian background is modeled by the mixed Gaussian model.
1046 在此基础上得到了限幅阈值与限幅检测性能之间的解析表达式， On this basis, the analytical expression between the clip-ping threshold and the detection performance is obtained.
1047 最后通过推导确定了最佳限幅阈值，明显提高了检测性能。 Finally, the optimal threshold is determined which leads to a signif-icantly improvement of the detection performance.
1048 仿真结果表明 ALMD 与 LOD 性能接近，但结构简单，性能稳健，适应性更强。 Simulation results shows that the ALMD has similar performance com-pared to LOD but provide relatively efficient and robustness.
1049 基于大气湍流非相干散射理论，采用泰勒方法对湍流谱函数进行近似，推导获得了对流层散射传输损耗与大气折射率结构常数的关系，即 L-C 模型; Based on the incoherent scatter theory of atmospheric turbulence, the turbulence spectrum is approximatedby the Taylor method, and the relationship between the tropospheric scattering transmission loss and the atmospheric structure constant of the refractive index is obtained, that is, L-C model.
1050 开展了对流层散射传播试验，基于 WRF( Weather Research and Forecas-ting) 数值模式对试验期间大气折射率结构常数进行预报; The troposcatter propagation experiment was carried out, and the atmospheric structure constant of the refractive index during the test was predicted based on the numerical weather pre-diction model of WRF ( Weather Research and Forecasting) .
1051 基于预报的大气折射率结构常数数据应用 L-C 模型预测对流层散射传输损耗，并与试验测试损耗值进行对比研究。 Based on the forecasted data of the atmospheric structure con-stant of the refractive index, the L-C model is used to predict the troposcatter transmission loss, and compared with the meas-ured loss.
1052 结果表明，应用 L-C 模型预测的损耗值与实测值变化趋势吻合较好，均方根误差不超过 6dB， It is shown that the variation tendency of the predicted loss and the measured loss are in good agreement, and theRMS ( root mean square) errors are no more than 6dB.