Low-altitude Wind Shear Detection Method Based on Dual Tunable Q-factor Wavelet Transform for Airborne Weather Radar

Abstract

Airborne weather radar is an important means of detecting low-altitude wind shear for modern civil aviation airliners. The accuracy of wind speed estimation is the key factor affecting the detection effect of low-altitude wind shear. In this paper, a novel method of wind shear detection based on dual tunable Q-factor wavelet transform(D-TQWT)is proposed. According to the different oscillation characteristics of ground clutter and low-altitude wind shear, the morphological component analysis method is used to sparsely represent the wind shear and the ground clutter signals on the wavelet basis functions with different quality Q factors, and the noise signal is eliminated by solving the BPD optimization problem. Then, referring to the frequency energy distribution characteristics of signals under different Q factors, the effective separation of wind shear and ground clutter signals is realized. Finally, the sparse wavelet coefficients representing the wind shear signal are reconstructed to obtain more accurate wind speed estimation results. The experimental results show that the difference of signal oscillation characteristics is used in this method for dual tunable Q-factor wavelet transform, which can effectively distinguish wind shear signal and ground clutter signal without designing the clutter suppression filter, especially in the close interval of frequency domain, so as to obtain more accurate anemometer results.