Study Of Space Registration Algorithm Based On Least Squares Class

Time-space registration includes time registration and space registration. It is an important part of information fusion in the sensor network system and plays a important role in the system's detection performance. Most scholars'researches tend to space registration with its complexity and universality. This paper based on space registration analyses some existing algorithms which is relatively mature and proposes own improved views and algorithms. Then, show the paper's main researches and achievements:(1) Combined with practical applications, this paper transplants some existing space registration into the single-platform sensor network system. By the basic theories of least squares, total least squares, it analyses the influent of the random error and system error in systematic error estimation model. Then the conclusion that there are statistical correlation errors in the coefficient matrix of measurement equation is given. According to this conclusion, the structured total least squares space registration algorithm is proposed. The simulation shows that the algorithm can effectively improve the accuracy of system error estimation.(2) Least squares type algorithm will be extended to multi-platform and multi-sensor network system. By bringing in the Earth-centered Earth-fixed coordinate transformation, the error caused by earth curvature can be removed. And the registration model is created in accordance with generalized least square which is commonly used in engineering. But we find that the established registration model is not accurate, so this paper amends this model. After this operation, we find the improved model isn't fit for generalized least squares. With using the basic theory of least squares type algorithm, this paper proposes the constrained total squares space registration algorithm based on the improved model.(3) If the registration model is not fully observed, the existing research results can't perfectly describe the quantitative relationship between observability and systematic error estimation accuracy. Even so, the system's observability can be improved by other indirect means. Meanwhile, it also raises the systematic error estimation precision. This paper uses the inverse of the coefficient matrix condition number as the observability index. Then filter the original measurement data can be filtered through the observability index to optimize the data in the sliding window. If you do so, the performance of the structured total least squares space registration can be improved.Finally, the whole dissertation is summarized and outlooked.