Application of Least Square Method with Variable Parameters for GPS Accuracy Improvement

摘要

Adaptive information processing methods are widely used in navigation techniques. Most often they are use for tow or more sensor information processing. GPS information is most often complex treated with inertial sensor information using adaptive filtering techniques [1, 2]. If navigation system has only one source of information adaptive filtering can be used [3]. Widely known are following algorithms: Least Mean Square (LMS) algorithm, Recursive Least Squares (RLS) algorithm and Kalman Filtering (KF) algorithm [3]. We research Least Square Method (LSM) for one source information filtering with sliding window. Length of window changes in filtering process and depends on the evaluation results. LSM algorithms for information processing in window are used from [4]. This work describes results of filter modeling and optimization and use of optimal sliding window filter for GPS information processing.