Proliferation Of Integrated Navigation Filtering And Detection Studies

The integrated navigation system of Strapdown Inertial Navigation System(SINS) and Global. Positioning System(GPS) has integrated the advantage of SINS and GPS and has advantages of high precision, good reliability, and low cost, to achieve the integration of multiple information and to improve navigation system redundancy and fault tolerance. The algorithm of restrain filter divergence can make the integrated navigation system filter work more stable. On the loose combination of the integrated navigation system, this thesis study in-depth on restrain divergence filtering methods and design of the simulation system to test the filtering effect.Firstly, this thesis describes the SINS/GPS integrated navigation filter algorithm development status, and then introduce basic principles of SINS and GPS, and establishes error models of strapdown inertial navigation system and GPS, and introduces the basic theory of kalman filter and application of the SINS/GPS Integrated Navigation System.Secondly, the loose combination of the SINS/GPS integrated navigation system was studied. Expatiate principles and characteristics of the loose combination of the navigation system, and subsequently established state equation and measurement equation of the SINS/GPS integrated navigation. And introduce the basic principle of divergence method.Finally, the SINS/GPS simulation integrated navigation system according to loose combination was designed. First simulate the trajectory curve of the movement entity, and simulate output of gyroscope and accelerometer and GPS data. Then based on error theory of strapdown inertial navigation system and principle of the GPS system to generate gyroscopes, accelerometers and GPS error data,and add this error data to the output of the gyroscopes and accelerometers. Then kalman filter, attenuation memory filter and adaptive restrain filter for integrated navigation system was designed. Experiment results show that the filtering algorithm of restrain divergence can get more good filtering results than kalman filter, and obtain smaller errors of three-dimensional position and velocity.