Research On Wind Speed And Direction Measurement Based On INS/Doppler Integrated Navigation

There is an increasing application of the unmanned aircraft vehicle (UAV) in the military and civilian areas However UAV's flight performances are constantly impacted by the wind field. For this reason, a technology of the wind measuring is presented in this thesis.The wind measuring method of plane vector triangle is proved to be more suitable for long-endurance, large-scale continuous flight, and is able to improve the performance of the flight safety based on analyzing the current several wind measuring methods. The principle and the error sources of the method are investigated, and the error models of wind speed and direction measuring are established and stimulated in this thesis.Airspeed measuring error is the primary factor in all the errors in the measurement of wind speed. The airspeed error model is established on the basis of analyzing airspeed error. The sensitivity of errors is studied by airspeed error model and simulation, the static pressure error is decided as the main factor influencing airspeed error, and the methods of reducing errors in engineering are suggested.Groundspeed error is another primary factor in all the errors in the measurement of wind speed. To further improve the precision of groundspeed measuring, the INS aided by Doppler is designed. Hence estimation approach which can estimate the parameter of Doppler velocity random error is also studied in this thesis. Based on correlation function and ARMA model, a new effective approach which can estimate the parameter of gauss white noise and first-order Markov noise is proposed. Furthermore, a prior knowledge of noise is not needed, which means the approach is suitable in most situations. The approach can offer exact parameter of inertial sensor in integrated navigation. On this basis, the mathematical model of INS/Doppler integrated navigation system is studied, for practical Doppler/inertial integrated navigation system, the extend Kalman filter with observation error is proposed. The calculation of practical data shows that, the method can effectively improve the velocity precision of Doppler/inertial integrated navigation system.The wind velocity data with UAV contains more continuous outliers, and the prior distribution of noise statistics is known insufficiently. To improve the precision of wind velocity, an adaptive Kalman filter (KF) algorithm with restraining outliers is investigated in this thesis. A compressibility function is integrated to new information based on analyzing the simplified Sage-Husa adaptive Kalman filter (AKF) algorithm. According to the changing of the variance and mean value of new information, the weighting factor is adjusted adaptively to ensure the initial properties. Simulation and analysis indicate that the algorithm can reduce the influence of outliers, and ensure the precision. The algorithm can be applied in UAV wind measurement.