Atmospheric turbulence detection by PCA approach

By detecting the severe meteorological situations on flight route, airborne weather radar (WXR) can ensure the safety of the aircraft and on-board personnel. Among these critical weather conditions, atmospheric turbulence is one of the main factors that affect flight safety. Atmospheric turbulence detection method that the current WXR adopts mainly is the pulse pair processing (PPP) method, which estimates Doppler spectrum width of weather target echo and compares it with a threshold to determine whether this weather target is turbulence or not. PPP method is simple and easy to implement, but the performance of this method under the condition of low signal-to-noise ratio (SNR) is poor. In this paper, we propose a new turbulence detection method based on the principal component analysis (PCA) approach. This new method uses PCA approach to preprocess the weather target echo and divides it into two parts: the principal component part as signal and the rest part as noise, so as to realize the de-noising function of PCA approach, and it is then combined with PPP method to estimate the spectrum width. Due to the good de-noising performance of PCA approach, this new method improves the detection performance of traditional PPP method especially under the condition of low SNR.