Laser Ranging-Based Detection Technology for Runway Contaminants at Cold Region Airports

During snowmelt, snow poses a significant safety hazard to aircraft takeoff and landing. When the airport runway surface is polluted by wet and dry snow, slush or frost, it will reduce the friction coefficient between the tire and the ground, resulting in reduced braking efficiency, and increase the taxi distance, interfering with the normal take-off and landing performance, thereby increasing the risk of flight operations. In this case, accurate and timely assessment of runway conditions becomes critical. However, due to the high moisture content and physical heterogeneity of slush, traditional manual measurement and snow thickness detection techniques are difficult to achieve accurate measurement. Therefore, this study proposes a rapid snow thickness detection method based on laser ranging technology. Considering the mixed composition characteristics of snow slush on airport surfaces, this study systematically explores the echo signal characteristics of wet snow and snow slush under different wavelengths. By analyzing the spectral reflectance curves, phase noise (PN), and signal amplitude of different snow samples, 650 nm and 850 nm wavelengths were identified as the most suitable for detection. A dual-wavelength laser snow thickness detection system was designed accordingly. This system utilizes frequency domain filtering, short-time Fourier transform (STFT), and Hilbert transform to process the non-stationary echo signals of snow slush and has been validated through field tests in various regions of northern China. Experimental results indicate that this system can attain high measurement accuracy across various meteorological conditions.