Study On Finite Approximation Truncation Algorithm And The Related Application Of Autonomous Navigation

Along with the development of information technology, the deep space detection technology is also advanced, the exploration of space has already broken through the earth-moon system and extended to more distant space. Especially, with the introduction of the visual navigation technology, autonomous navigation is developing rapidly. In autonomous navigation, image feature point extraction and tracking, and the building of essential matrix, as well as the planetary precise landing problems often involve large linear equations, or large matrix, fast and efficient solving scheme has not only theoretical significance, but also has practical value. What’s more, for many practical problems, for example, metallurgical engineering, large-scale civil structure, optimal control, image processing, economic planning, population reproduction, large transmission network can be summed up in solving large linear equations. Moreover, as the problem size increases, the corresponding linear equations has greatly increased. These large linear equations solving is the basis and key to solving the problem, its computation also accounts for rather proportion of the whole calculation process. Large linear equations solving study is one of the important subject of modern scientific computing, and also one of the focus attention. The purpose of this paper is to study high-performance algorithm for solving large linear system, and applying the algorithm for solving the problems of the autonomous navigation. The following is the paper’s research contents and results:1)At first, this paper introduces the application background of large linear equations, especially in the application of visual navigation direction. Secondly, this paper introduces the principle of limited truncation algorithm and the related properties of matrix equation, and on the basis of the large-scale system of linear equations truncated solutions puts forward three kinds of, the truncation scheme based on the norm truncation scheme, based on the condition number truncation scheme, truncation scheme based on the row norm and condition number.2)Through a series of examples of large matrix and experiment data to illustrate the effectiveness of the algorithm, as well as, this paper also explains the pros and cons of various algorithms, in contrast to reach more effective truncation scheme.3) Algorithm was applied to the related problems in actual navigation, illustrating the feasibility practical significance of the algorithm.