Abstract
Owing to their numerous merits, such as compact, autonomous and independence, the strapdown inertial navigation system (SINS) and celestial navigation system (CNS) can be used in marine applications. What is more, due to the complementary navigation information obtained from two different kinds of sensors, the accuracy of the SINS/CNS integrated navigation system can be enhanced availably. Thus, the SINS/CNS system is widely used in the marine navigation field. However, the CNS is easily interfered with by the surroundings, which will lead to the output being discontinuous. Thus, the uncertainty problem caused by the lost measurement will reduce the system accuracy. In this paper, a robust filter based on the Krein space theory is proposed. The Krein space theory is introduced firstly, and then, the linear state and observation models of the SINS/CNS integrated navigation system are established reasonably. By taking the uncertainty problem into account, in this paper, a new robust filter is proposed to improve the robustness of the integrated system. At last, this new robust filter based on the Krein space theory is estimated by numerical simulations and actual experiments. Additionally, the simulation and experiment results and analysis show that the attitude errors can be reduced by utilizing the proposed robust filter effectively when the measurements are missing discontinuous. Compared to the traditional Kalman filter (KF) method, the accuracy of the SINS/CNS integrated system is improved, verifying the robustness and the availability of the proposed robust filter.
Highlights
In modern marine navigation, the strapdown inertial navigation system (SINS) is widely used due to its advantages of being more compact and autonomous, which can provide vehicle’s navigation information, including attitude, velocity and position [1,2,3]
Position System (GPS), the Doppler velocity log (DVL), the celestial navigation system (CNS), etc., are often integrated with it to improve the navigation accuracy of the whole system availably making use of the complementary navigation information obtained from different sensors [4,5,6,7,8]
In order to solve the uncertainty problem of the SINS/CNS integrated navigation system caused by the missing measurements, a novel robust H∞ filter based on the Krein space theory was proposed in this manuscript
Summary
The strapdown inertial navigation system (SINS) is widely used due to its advantages of being more compact and autonomous, which can provide vehicle’s navigation information, including attitude, velocity and position [1,2,3]. In [9], Xu and Fang proposed INS/CNS integration, using the INS error model and Kalman filter (KF) based on neural networks. Hamza and Nebylov proposed a robust design of an INS/GNSS navigation system to solve the problem of state space models with non-Gaussian measurement noise based on parallel nonlinear filtering [13]. A robust H∞ filter for the SINS/CNS integrated navigation system is presented in this manuscript based on the Krein space theory. The results from simulations and experiments show that the presented robust filter is superior to the normal Kalman filter The rest of this manuscript is organized as follows.
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