To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwat...To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwater terrainmatching data. An underwater terrain interpolation error compensation method based on fractional Brownian motion is proposed for defects of normal terrain interpolation, and an underwater terrain-matching positioning method based on least squares estimation(LSE) is proposed for correlation analysis of topographic features. The Fisher method is introduced as a secondary criterion for pseudo localization appearing in a topographic features flat area, effectively reducing the impact of pseudo positioning points on matching accuracy and improving the positioning accuracy of terrain flat areas. Simulation experiments based on electronic chart and multi-beam sea trial data show that drift errors of an inertial navigation system can be corrected effectively using the proposed method. The positioning accuracy and practicality are high, satisfying the requirement of underwater accurate positioning.展开更多
In this paper, the multivariate linear model Y = XB+e, e ~ Nm×k(0, ImΣ) is considered from the Bayes perspective. Under the normal-inverse Wishart prior for (BΣ), the Bayes estimators are derived. The sup...In this paper, the multivariate linear model Y = XB+e, e ~ Nm×k(0, ImΣ) is considered from the Bayes perspective. Under the normal-inverse Wishart prior for (BΣ), the Bayes estimators are derived. The superiority of the Bayes estimators of B and Σ over the least squares estimators under the criteria of Bayes mean squared error (BMSE) and Bayes mean squared error matrix (BMSEM) is shown. In addition, the Pitman Closeness (PC) criterion is also included to investigate the superiority of the Bayes estimator of B.展开更多
基金financially supported by the National Natural Science Foundation of China(Grant Nos.51179035 and 51279221)the Natural Science Foundation of Heilongjiang Province(Grant No.E201121)
文摘To achieve accurate positioning of autonomous underwater vehicles, an appropriate underwater terrain database storage format for underwater terrain-matching positioning is established using multi-beam data as underwater terrainmatching data. An underwater terrain interpolation error compensation method based on fractional Brownian motion is proposed for defects of normal terrain interpolation, and an underwater terrain-matching positioning method based on least squares estimation(LSE) is proposed for correlation analysis of topographic features. The Fisher method is introduced as a secondary criterion for pseudo localization appearing in a topographic features flat area, effectively reducing the impact of pseudo positioning points on matching accuracy and improving the positioning accuracy of terrain flat areas. Simulation experiments based on electronic chart and multi-beam sea trial data show that drift errors of an inertial navigation system can be corrected effectively using the proposed method. The positioning accuracy and practicality are high, satisfying the requirement of underwater accurate positioning.
基金Supported by National Natural Science Foundation of China(Grant Nos.11201005,11071015)the Foundation of National Bureau of Statistics(Grant No.2013LZ17)the Natural Science Foundation of Anhui Province(Grant No.1308085QA13)
文摘In this paper, the multivariate linear model Y = XB+e, e ~ Nm×k(0, ImΣ) is considered from the Bayes perspective. Under the normal-inverse Wishart prior for (BΣ), the Bayes estimators are derived. The superiority of the Bayes estimators of B and Σ over the least squares estimators under the criteria of Bayes mean squared error (BMSE) and Bayes mean squared error matrix (BMSEM) is shown. In addition, the Pitman Closeness (PC) criterion is also included to investigate the superiority of the Bayes estimator of B.