An Algorithm Of Integrated Navigation For A 4-DOF Air Flotation System

It is a trend to make a navigation system in combination with others. Relative to a single navigation system the integrated navigation system can achieve complementary functions among the various subsystems, so it can improve the stability, accuracy and real-time of the system. Meanwhile, by measuring the redundancy value it makes the system more reliability. Against this background a further study has been taken to make out the method for integrated navigation based on inertial navigation and vision.Firstly, this thesis studies a specific filter algorithm for inertial navigation system. Thanks to the features such as autonomy and Imperceptibility, inertial navigation system has been widely used in the navigation area. However, it is usually corrupted by initial condition errors, noise, bias and drift variation, so that the navigation errors may be accumulated and lead to significant errors. Although it can be used in a short time, the precision will be low due to the influence of many errors. In order to reduce effects of various kinds of interference and improve accuracy, kalman filtering algorithm is introduced when deal with the data from inertal navigation system. We can get the accuracy attitude and position information of the object relative to inertial reference frame real-time by solving with the data filtered. This method has been proved effective by the later simulation.Secondly, the algorithm to get the attitude and position information based on vision has been studied. As a new navigation technology, vision is used widely due to it's stability and high accuracy. There are many ways to get navigation information with the measure value of the camera. According to the design of integrated navigation system, we choose the Gaussian Least Square Differential Correction to determine the optimal parameters of the collinear function of the line-of-sight vector. This method has been proved effective by the later simulation.Finally, we get the specific algorithm for the combination of inertial navigation system and vision. Both inertial navigation system and vision all get two sides, inertial navigation system can capture the fast dynamics of a maneuverable vehicle but it can't work stability in a long time; vision measured system can work stability but it has a low bandwidth. We can get a useful navigation system by using them simultaneously. The algorithm which has been used to assemble inertial navigation system and vision measured system together is extend kalman filtering. And this navigation algorithm has been fully developed and its feasibility is demonstrated through numerical simulations.