Xixiang Liu

A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles.

Kalman Filter for Cross-Noise in the Integration of SINS and DVL

The integration of strapdown inertial navigation system and Doppler velocity log (SINS/DVL) is widely used for navigation in automatic underwater vehicles (AUVs). In the integration of SINS/DVL, the velocity measured by DVL in body frame should be projected into navigation frame with the help of attitude matrix calculated by SINS to participate in data fusion. In the process of data fusion based on standard Kalman filter, the errors in calculated attitude matrix are characterized by state variance and process noise while the errors in measurement vector from DVL are by measurement noise. But the above projection will bring process noise into measurement noise, and thus the assumption of the independence between process noise and measurement noise will not stand. In this paper, the forming mechanism of cross-noise in SINS/DVL is studied in detail and Kalman filter for cross-noise is introduced to deal with this problem. Simulation results indicate that navigation accuracy, especially the position accuracy, can be improved when the cross-noise is processed in Kalman filter.

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.

A Rapid Transfer Alignment Method for SINS Based on the Added Backward-Forward SINS Resolution and Data Fusion

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.

A Fast and High-Accuracy Compass Alignment Method to SINS with Azimuth Axis Rotation

Azimuth axis rotating modulation was introduced to improve the alignment accuracy of strapdown inertial navigation system (SINS) through compass algorithm, in which the limit accuracy was determined by equivalent sensor errors in the eastern and northern direction. In this modulation, horizontal sensor errors were modulated into zero mean periodic variables. Furthermore, two methods were introduced to ensure alignment accuracy and speed: (1) shortened rotating cycle and redesigned compass parameters were selected to eliminate or ease the amplification to low-frequency senor error inputs in compass loop caused by rotation and (2) a data repeated calculation method was designed to shorten prolonged alignment time caused by the above redesigned parameters. Based on a certain SINS, turntable test proves that alignment accuracy and time were significantly improved and slightly shortened in comparison with the classical compass alignment.

A novel self-alignment method for SINS based on parameter recognition and dual-velocity vectors

In this paper, self-alignment problem for strapdown inertial navigation system based on tracing gravity drift in inertial frame and dual-vector attitude determination is studied. Aiming to further ease, the interference from sensor random errors, especially accelerometer random noise, without adding alignment time, a novel alignment method based on integrating gravitational apparent motion to form apparent velocity is designed, using recursive least squares algorithm to recognize the parameters describing the theoretical apparent velocity from the calculated velocity containing random noise. Analysis and simulation indicate that with this method, random noise can be effectively removed compared with the only operation of integration. Meanwhile, a reconstruction algorithm with current recognized parameters for dual-velocity vectors is devised which can fully utilize all measurement information and completely avoid collinear problem. Simulation and turntable results show that compared with the existing integrating methods, the proposed method can acquire sound alignment results with lower standard variances and can improve alignment accuracy with the same alignment time or shorten alignment time with the same accuracy.

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.