Detection Of Clear Air Turbulence Based On Vertical Load Factor

Clear air turbulence(CAT),what is called the “invisible killer”,is a hazardous weather threatening in-flight aircrafts safety,and researches suggest that the amount of CAT will increase significantly in the future with global warming grows.CAT is turbulence which occurs in regions absence of significant cloudiness,thus it is very difficult to detect the CAT using current airborne weather radars which are widely equipped in civil aviation,and these radars detect turbulence only based on spectrum width of the echoes.Specifications of turbulence detection for airborne weather radar have been complemented in the latest revised DO-220 A by American Radio Technical Commission for Aeronautics.This version states that the characteristics of aircraft should be taken into account in the turbulence detection.According to these specifications,a CAT detection method based on the vertical load factor is proposed in this thesis,which is aiming to accurately detect the intensity and areas of CAT occurrence using airborne weather radar.The impact of CAT on an aircraft during the flight is analyzed in the first place.Theoretical investigation on the relationship between the vertical load factor and the damage of CAT to the aircraft is conducted.And the analysis of ADS-B surveillance data of real flights verifies the rationality of our proposed theory.Then,a CAT detection process for airborne weather radar with the vertical load factor as an indicator is designed.An enhanced turbulence detection method based on vertical load factor is also investigated.And the threshold of turbulence detection based on statistical characteristics is obtained by applying the Bayesian criterion.On this basis,a refined version of enhanced turbulence detection is designed.Simulation results show that this refined version can fulfill the requirements in accordance with DO-220 A.Furthermore,in order to estimate the spectral width of the radar echoes,which will directly affect the vertical load factor,a spectral width estimation algorithm in low SNR scenarios is also proposed.This method aims to address the problem of poor spectral width estimation caused by the low SNR echoes received by the widely used airborne weather radars when trying to detect CAT.Based on reduced-rank multistage wiener filter(RR-MWF),this adaptive method can improve the SNR of radar echoes by integrating coherently both in the spatial and the temporal dimension.The adaptive RR-MWF weighted vector is constructed for estimating the spectral width of the radar echoes reflected by distributed targets,such as CAT.Simulations show that the proposed method can reduce the computational complexity while effectively estimating the spectral width of radar echoes in low SNR scenarios.