Improving water/ice/snow depth accuracy on runway pavement with ultrasonic echo signal analysis

Abstract

With the widespread adoption of Global Report Format (GRF) in airports worldwide, accurately measuring contaminant depth is crucial for runway skid resistance. This study employed ultrasonic technology to obtain different contaminants depth. By analyzing the echo waveform characteristics of contaminants, the types of contaminants were successfully identified using a neural network. For enhancing the accuracy of Time-of-Flight (ToF) estimation, the Optimal Multi-Objective Particle Swarm Optimization (OMOPSO) method was proposed for echo parameters estimation. Simulation tests revealed the Pareto solution set of OMOPSO was evenly distributed, demonstrating good convergence in estimating parameters of distorted noisy signals, and exhibiting excellent fitting results for the signal rising edge. The results of the contaminants depth experiment conducted in airport pavement weather condition room shown varying contaminants illustrating different depth measurement accuracy but all within 1.67 mm average error, and demonstrated the OMOPSO approach, which considered various waveform characteristics, exhibited robustness to waveform distortion and noise, outperformed traditional methods.