Yiting Liu

Initial Alignment of Large Azimuth Misalignment Angles in SINS Based on Adaptive UPF

The case of large azimuth misalignment angles in a strapdown inertial navigation system (SINS) is analyzed, and a method of using the adaptive UPF for the initial alignment is proposed. The filter is based on the idea of a strong tracking filter; through the introduction of the attenuation memory factor to effectively enhance the corrections of the current information residual error on the system, it reduces the influence on the system due to the system simplification, and the uncertainty of noise statistical properties to a certain extent; meanwhile, the UPF particle degradation phenomenon is better overcome. Finally, two kinds of non-linear filters, UPF and adaptive UPF, are adopted in the initial alignment of large azimuth misalignment angles in SINS, and the filtering effects of the two kinds of nonlinear filter on the initial alignment were compared by simulation and turntable experiments. The simulation and turntable experiment results show that the speed and precision of the initial alignment using adaptive UPF for a large azimuth misalignment angle in SINS under the circumstance that the statistical properties of the system noise are certain or not have been improved to some extent.

FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter

In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

A Self-Alignment Algorithm for SINS Based on Gravitational Apparent Motion and Sensor Data Denoising

Initial alignment is always a key topic and difficult to achieve in an inertial navigation system (INS). In this paper a novel self-initial alignment algorithm is proposed using gravitational apparent motion vectors at three different moments and vector-operation. Simulation and analysis showed that this method easily suffers from the random noise contained in accelerometer measurements which are used to construct apparent motion directly. Aiming to resolve this problem, an online sensor data denoising method based on a Kalman filter is proposed and a novel reconstruction method for apparent motion is designed to avoid the collinearity among vectors participating in the alignment solution. Simulation, turntable tests and vehicle tests indicate that the proposed alignment algorithm can fulfill initial alignment of strapdown INS (SINS) under both static and swinging conditions. The accuracy can either reach or approach the theoretical values determined by sensor precision under static or swinging conditions.

An Adaptive Damping Network Designed for Strapdown Fiber Optic Gyrocompass System for Ships

The strapdown fiber optic gyrocompass (strapdown FOGC) system for ships primarily works on external horizontal damping and undamping statuses. When there are large sea condition changes, the system will switch frequently between the external horizontal damping status and the undamping status. This means that the system is always in an adjustment status and influences the dynamic accuracy of the system. Aiming at the limitations of the conventional damping method, a new design idea is proposed, where the adaptive control method is used to design the horizontal damping network of the strapdown FOGC system. According to the size of acceleration, the parameters of the damping network are changed to make the system error caused by the ship’s maneuvering to a minimum. Furthermore, the jump in damping coefficient was transformed into gradual change to make a smooth system status switch. The adaptive damping network was applied for strapdown FOGC under the static and dynamic condition, and its performance was compared with the conventional damping, and undamping means. Experimental results showed that the adaptive damping network was effective in improving the dynamic performance of the strapdown FOGC.

A fast mutant fault detection method of underwater integrated navigation

The concepts of the mean of residual and the sum of absolute residuals are introduced in this paper. Both of them along with the fault detection function are used to detect mutant fault of the assisted navigation devices in underwater integrated navigation system. (1) The traditional residual χ2 detection method is improved according to the structural characteristics of the underwater navigation system. (2) The mean of residual and the sum of absolute residuals are introduced as the characteristics of mutant faults. (3) The improved residual χ2 detection method is used to make sure of the happening of the mutant fault firstly. Then the residual mean and the sum of residual absolute values are used to distinguish the noise mutant system fault and the information mutant system fault. The results of simulations and the vehicle tests show that: (1) the mutant fault of the sub-system can be detected and the faulty sub-system can be isolated immediately when the fault appears with our novel method; (2) The noise mutant fault and the information mutant fault can be distinguished efficiently according to characteristics of the mean of residuals and the sum of the absolute residuals.

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.