Xiaosu Xu

H∞ filter for flexure deformation and lever arm effect compensation in M/S INS integration

Abstract On ship, especially on large ship, the flexure deformation between Master (M)/Slave (S) Inertial Navigation System (INS) is a key factor which determines the accuracy of the integrated system of M/S INS. In engineering this flexure deformation will be increased with the added ship size. In the M/S INS integrated system, the attitude error between MINS and SINS cannot really reflect the misalignment angle change of SINS due to the flexure deformation. At the same time, the flexure deformation will bring the change of the lever arm size, which further induces the uncertainty of lever arm velocity, resulting in the velocity matching error. To solve this problem, a H∞ algorithm is proposed, in which the attitude and velocity matching error caused by deformation is considered as measurement noise with limited energy, and measurement noise will be restrained by the robustness of H∞ filter. Based on the classical “attitude plus velocity” matching method, the progress of M/S INS information fusion is simulated and compared by using three kinds of schemes, which are known and unknown flexure deformation with standard Kalman filter, and unknown flexure deformation with H∞ filter, respectively. Simulation results indicate that H∞ filter can effectively improve the accuracy of information fusion when flexure deformation is unknown but non-ignorable

The Application of H2/H-Infinity Filter in Transfer Alignment Based on Convex Optimization Technology

In this paper, the “velocity and attitude” matching method is used and the unknown flexure deformation is treaded as the noise with limited energy. Algorithms of H∞ filter, adaptive H∞ filter and H₂/H∞ filter are introduced and compared. Results show that: the alignment accuracy of adaptive H∞ filter is the highest, the alignment accuracy of H₂/H ∞ filter is suboptimal when the unknown flexure deformation exists. While the alignment accuracy of H₂/H∞ filter is the highest when there is no unknown flexure deformation. From the overall consideration, H₂/H∞ filter is the optimal when the unknown flexure deformation exists.

Research of Autonomous Navigation System for AUV Based on SDVM

Considering that the underwater condition is not accessible to radio signals, this paper proposed a novel autonoums integrated navigation system using the output from an simplified dynamic vehicle model (SDVM) to provide velocity aiding for the strap down inertial navigation system (SINS), and the terrain-aide navigation system (TAN) is engaged to prevent the position divergence. The proposed approach improves underwater navigation capabilities both for systems lacking conventional velocity measurements, and for systems where the need for redundancy is important, such as when AVU travels to the mismatching area and TAN dropouts or failures. Compared experiments are perfermed to prove the effectiveness of this method. The results show that the proposed navigation system is able to keep the sustainability and accuracy at a satisfactory level even during long time periods without TAN measurements.

Novel terrain integrated navigation system using neural network aided Kalman filter

A terrain integrated navigation system is proposed to adapt the characteristics of the underwater environment and high accuracy requirements of AUV navigation, which is composed of the strapdown inertial navigation system (SINS), Terrain-aided navigation system (TAN),the Doppler velocity log (DVL) and the magnetic compass(MCP). An improved federated Kalman filter based on the back-propagation neural network(BPNN) for adjusting the information sharing factors is designed and implemented in the AUV integrated navigation system. Linear filter equations for the Kalman filter and measurement equations of navigation sensors are addressed. Simulation experiments are carried out according to the mathematic model. The comparable results indicate that the AUV navigation precision and adaptive capacity are improved substantially with the proposed sensors and the intelligent Kalman filter.