Research On Atmospheric Turbulence Detection Method For Airborne Weather Radar

Airborne weather radar(WXR),as a core subsystem of the aircraft environment surveillance system(AESS),is an on-board equipment of civil aircraft that must be equipped to comply with FAA/EASA/CCAR25.It can detect the severe weather conditions on the flight path of the aircraft and provide warning information to the pilot in real time,which can ensure the safety of the aircraft and the crew on the flight during the whole flying process.With the development of the new functions of airborne equipment,especially the integration of various airborne equipment under the IMA architecture,it is necessary to carry out theoretical research on system model,data processing and ground simulation of airborne weather radar.Under this background,this paper focuses on the new signal processing method of airborne weather radar in detecting atmospheric turbulence,and proposes two atmospheric turbulence detection methods based on neural network and principal component analysis.The main content includes the following four aspects:1.The typical airborne weather radar system is introduced,including the basic structure and system functions of airborne weather radar,the harmful principle of atmospheric turbulence on aircraft flight and the commonly used atmospheric turbulence detection methods.2.A method of atmospheric turbulence detection based on BP neural network is proposed.By using the multi-class classification function of BP neural network,the radar echo data of weather targets are directly classified and processed,so as to achieve the purpose of detecting atmospheric turbulence.The problems of pulse pair method,fast Fourier transform method and model fitting method for classical atmospheric turbulence detection are solved.The proposed method can accurately identify the intensity or presence of atmospheric turbulence without using empirical formulas and complex parametric models.The experimental results show that the proposed method has good accuracy when classifying the four intensity levels of atmospheric turbulence.When it is only necessary to judge whether or not atmospheric turbulence exists,the accuracy rate is greatly improved.And in the case of low signal to noise ratio,the proposed method still has good detection performance.3.A atmospheric turbulence detection method based on principal component analysis is proposed.Using the good noise reduction capability of principal component analysis,before the spectral moment estimation of the weather target radar echo signal by pulse pair method,the principal component analysis method is used to denoise the original data,and then combining with the pulse pair method to estimate the spectrum width for the purpose to detect the intensity of atmospheric turbulence.The experimental results show that the proposed method has better detection performance than the traditional pulse method,regardless of the length of the echo data and the signal-to-noise ratio,which overcomes the problem that the traditional pulse pair method has poor detection performance under low SNR.4.A airborne weather radar simulation software is established under the laboratory conditions,including weather radar data excitation module,weather radar control panel and integrated display interface.Under the excitation of weather data,the simulation software can detect common weather conditions and display them in real time,and when turbulence or wind shear occurs,the warning information can be given in real time too.