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飞机尾流探测技术是航空安全领域的重要研究课题。使用一组均匀分布的粒子模拟尾流涡旋的运动特征,研究了潮湿大气中尾涡多普勒谱的视频回波仿真方法。尾流回波的信噪比很低,降低检测门限的方法导致产生大量虚警。为了克服这个缺点,基于尾涡多普勒特性,提出了一种利用多层前馈神经网络提取尾流回波的方法。仿真表明,尾流回波的多普勒谱谱宽较大,谱型不同于气象回波的高斯谱,通过对比分析验证了神经网络提取方法的有效性。研究结果有助于利用脉冲多普勒雷达对飞机尾流探测技术的研究。
Aircraft wake detection technology is an important research topic in the field of aviation safety. A set of uniformly distributed particles is used to simulate the wake vortex motion characteristics. The simulation method of the video echo of the wake vortex Doppler spectrum in moist atmosphere is studied. The signal-to-noise ratio of the wake echo is very low, and the method of lowering the detection threshold results in a large number of false alarms. In order to overcome this shortcoming, a method based on wake vortex Doppler characteristic is proposed to extract wake turbulence by using multilayer feedforward neural network. The simulation shows that the wake echo has a large Doppler spectral spectrum and a spectral pattern different from the Gaussian spectrum of the meteorological echo. The comparison and analysis verify the effectiveness of the neural network extraction method. The research results are helpful to the research of aircraft wake detection technology using pulsed Doppler radar.