The Integrated navigation numerical correction and prediction of autonomous underwater vehicle (AUV) based on GM (1, 1) model

Abstract

Navigation accuracy is one of the crucial performances for autonomous underwater vehicle (AUV), and is an important indicator of evaluating AUV performance. Typically, it is achieving AUV underwater navigation that adopting Doppler velocity log (DVL) integrated Strap down inertia Navigation System (SINS). To improve navigation accuracy of SINS / DVL, this paper embarks from the SINS / DVL integrated navigation model, to sets up filtering state equations and the measurement equation of integrated navigation system, then the grey prediction model GM (1, 1) is added to SINS / DVL integrated navigation system, and the grey prediction algorithm is applied to integrated navigation data analysis. Using the grey prediction model to predicate and correct the navigation data and, further improve the robustness and accuracy of the navigation system. Combined with actual project, the integrated navigation numerical correction and prediction of autonomous underwater vehicle (AUV) based on GM (1, 1) model is proposed. With the relative navigation error as the evaluation index, analyzes the navigation data, calculates relative navigation error of around predication and correction based on GM (1, 1) model; From the result, the introduction of grey prediction model carried on the forecast and the revision to the navigation data may increase the SINS/DVL navigation precision to a certain extent, it explained the accuracy and the usability of the method.