New filtering method for GPS/INS integrated navigation

Application of Federated Filter to AUV Based on Terrain-Aided SINS

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.711.338 ◽

2014 ◽

Vol 711 ◽

pp. 338-341 ◽

Cited By ~ 1

Author(s):

Qi Wang ◽

Cheng Shan Qian ◽

Zi Jia Zhang ◽

Chang Song Yang

Keyword(s):

Kalman Filtering ◽

Navigation System ◽

Autonomous Underwater Vehicles ◽

Inertial Navigation ◽

Integrated Navigation ◽

Integrated Navigation System ◽

Filtering Method ◽

Long Time ◽

Federated Filtering ◽

Strapdown Inertial Navigation

To improve the navigation precision and reliability of autonomous underwater vehicles, a terrain-aided strapdown inertial navigation based on Federated Filter (FF) is proposed in this paper. The characteristics of strapdown inertial navigation system and terrain-aided navigation system are described in this paper, and Federated Filtering method is applied to the information fusion. Simulation experiments of novel integrated navigation system proposed in the paper were carried out comparing to the traditional Kalman filtering methods. The experiment results suggest that the Federated Filtering method is able to improve the long-time navigation precision and reliability, relative to the traditional Kalman Filtering method.

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A Novel Adaptive Integrated Navigation Filtering Method Based on ARMA/GARCH Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.462-463.259 ◽

2013 ◽

Vol 462-463 ◽

pp. 259-266

Author(s):

Xin Zhao ◽

Hong Lei Qin ◽

Li Cong

Keyword(s):

Moving Average ◽

Garch Model ◽

Main Idea ◽

Arma Model ◽

Statistical Characteristics ◽

Autoregressive Moving Average ◽

Integrated Navigation ◽

Conditional Mean ◽

Filtering Method ◽

Residual Series

This paper proposes a novel adaptive integrated navigation filtering method based on autoregressive moving average (ARMA) model and generalized autoregressive conditional heteroscedasticity (GARCH) model. The main idea in this study is to employ ARMA/GARCH model to estimate statistical characteristics of filtering residual series online, namely, the conditional mean and conditional standard deviation, and then the filter parameters are adaptively adjusted based on forecasted results of ARMA/GARCH model in order to improve the reliability of the system when there are abnormal disturbance and other uncertain factors in real condition. On this basis, experiment is used to verify the validity of the method. The simulation results demonstrate that the ARMA/GARCH model can well capture the unusual condition of GPS receiver output, and this adaptive filtering method can effectively improve the reliability of the system.

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