Large Azimuth Misalignment Angle Research Articles

Rapid Reduction Dimensionality Transfer Alignment Method Based on CKF for SINS of Ship-borne Aircraft with GNSS Assistance

Abstract Aiming at the problem of rapid transfer alignment of inertial navigation system in shipborne environment with large azimuth misalignment angle, a dimension reduction fast transfer alignment scheme of shipborne aircraft inertial navigation system based on CKF algorithm is designed. Firstly, the nonlinear attitude error equation, velocity error equation and position error equation of the Global Navigation Satellite System (GNSS) assisted strapdown inertial navigation system in the case of large azimuth misalignment are analyzed. Secondly, the nonlinear dimensionality reduction rapid transfer alignment state equation and measurement equation are derived. Finally, the CKF algorithm is simulated and verified, and compared with EKF algorithm and UKF algorithm. The simulation results show that the reduced dimension fast transfer alignment method of shipborne aircraft inertial navigation system based on CKF algorithm can complete the estimation of attitude error angle in 80s, with horizontal estimation error of 1’ and azimuth error of 4’, and the estimation accuracy is equivalent to EKF and UKF, with less calculation and better rapidity.

An improved initial alignment method based on adaptive robust CKF algorithm

When the misalignment angle is a large angle in the strapdown inertial navigation system (SINS), it is necessary to establish a nonlinear error model to estimate the error. Hence, an improved initial alignment method based on adaptive robust CKF algorithm is proposed in this paper. Firstly, based on the analysis results, SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, adaptive robust CKF algorithm is designed. Finally, according to simulation analysis and experiment, adaptive robust CKF algorithm can augment the stability, improve filter estimation accuracy and convergence rate, which significantly improves the initial alignment ability of strapdown inertial navigation system at large azimuth misalignment angle.

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate.

Odometer Aided SINS in-Motion Alignment Method Based on Backtracking Scheme for Large Misalignment Angles

In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for odometer aided navigation system. It is difficult to obtain an accurate attitude within a short time using coarse alignment under intense maneuvering condition, so one of the main obstacles during in-motion alignment for SINS/OD is to estimate an accurate attitude during fine alignment process under large azimuth misalignment angle. However, most current in-motion alignment methods were based on linear error model. In this paper, an OD aided in-motion alignment method based on backtracking scheme for large misalignment angles is proposed. Three improvements were made as the following: (1) A continuous nonlinear 16-dimensional (16D) SINS/OD error model is derived. (2) We proposed a backtracking-based Extended Kalman filter (EKF) and (3) a strict reverse navigation method is utilized in the backward process. Experimental results illustrated that the proposed method can autonomously improve the estimated attitude accuracy under the condition of large misalignment angle.

Research on hull deformation measurement for large azimuth misalignment angle based on attitude quaternion

Hull deformation is the fundamental reason that restricts the establishment of a unified attitude reference for large ships, it can be effectively measured by inertia matching method with two inertia measurement sensors. However, due to the limitation of installation location, the azimuth misalignment angle between two inertial measurement sensors(IMSs) is large, which will result in the existing inertial matching linear model no longer applicable. Therefore, the large azimuth misalignment angle is separated separately, and an IMS coordinate system is rotated virtually by this angle, then the nonlinear deformation measurement model based on attitude quaternion is established. Finally, the hull deformation is estimated by combining online neural network and nonlinear filter. Simulation results demonstrate that, the proposed method can estimate the deformation accurately with large azimuth misalignment angle.

Application of improved fifth-degree cubature Kalman filter in the nonlinear initial alignment of strapdown inertial navigation system.

This paper addresses the state estimation of the nonlinear initial alignment of the strapdown inertial navigation system (SINS), which mainly focuses on the initial alignment on the swaying base and under the in-motion condition with the measurement uncertainties. In order to achieve a higher alignment precision, stronger numerical stability, and lower computational cost for the initial alignment of SINS on the swaying base, a new discrete large azimuth misalignment error model of SINS is established, and an improved fifth-degree cubature Kalman filter (5th-CKF) algorithm is proposed, which combines the 5th-CKF and a simplified dimensionality reduction filtering algorithm. The 5th-CKF is introduced to solve the nonlinear filtering problem, a simplified dimensionality reduction algorithm is derived to reduce the large calculation values of 5th-CKF. Furthermore, under the Bayesian framework, a novel filtering approach named the fifth-degree variational Bayesian (VB) adaptive cubature Kalman filter is deduced for the in-motion alignment with a large azimuth misalignment angle and unknown and time-varying measurement noise statistics, which combines the iterative VB approach and 5th-CKF. The 5th-CKF is exploited to handle the nonlinear initial alignment model, and the VB approach is utilized to iteratively estimate the sufficient statistics of the measurement noise. Mathematical simulation, turntable, and vehicle experiments are performed to demonstrate the effectiveness and the superiority of the proposed approaches.

Initial Alignment Algorithm Based on the DMCS Method in Single-Axis RSINS with Large Azimuth Misalignment Angles for Submarines.

Since the inertial sensor error has been modulated effectively by the Rotation Modulation Technique (RMT), the Rotation Strapdown Inertial Navigation System (RSINS) has been widely used for submarines in order to satisfy the requirement of high-accuracy and long working duration. The performance of the initial alignment is main factor affecting the accuracy of the Strapdown Inertial Navigation System (SINS). The traditional initial alignment algorithm based on the compass method has bad performance when the misalignment angle is large, which will make the submarine SINS fail to launch properly in a complex operating environment. Since the RSINS uses the mathematical platform to calculate the navigation information, it allows multiple algorithms to run simultaneously, and different algorithms do not interact with each other. Thus, to improve the alignment accuracy, an initial alignment algorithm based on the Dual Mathematical Calculation System (DMCS) is proposed; moreover, to solve the problem of large azimuth misalignment angle, an improved DMCS-based alignment algorithm is also presented in this paper. Both simulations and experiments showed that the novel algorithm can effectively improve the initial alignment performance under the large misalignment angle environment, enhancing the environmental suitability of the RSINS.

Initial Alignment of Large Azimuth Misalignment Angle in SINS based on Reduced Multiple Fading Factors Strong Tracking CKF

Aiming at the problem that Cubature Kalman Filter(CKF) has low accuracy and robustness under the condition of strapdown inertial navigation system(SINS) initial alignment due to model error and external disturbance, Reduced Multiple Strong Tracking Cubature Kalman Filter(RMSTCKF) is proposed, and the algorithm flow and sub-optimal solution of multiple fading factor are derived. Multiple fading factor can improve tracking ability under each state according to the degree of uncertainty of different states, having stronger adaptability and robustness. Applying RMSTCKF to large azimuth misalignment angle error function described by Euler platform error angle(EPEA), carrying out the simulation under two different conditions, namely noise mismatch and the base is disturbed, and making contrast between RSTCKF and RCKF, the simulation results show that the filter accuracy and convergence rate of RMSTCK when system noise mismatches with true noise are obviously better than RSTCKF and RCKF, having better practical value in engineering.

Initial Alignment for SINS Based on Pseudo-Earth Frame in Polar Regions.

An accurate initial alignment must be required for inertial navigation system (INS). The performance of initial alignment directly affects the following navigation accuracy. However, the rapid convergence of meridians and the small horizontal component of rotation of Earth make the traditional alignment methods ineffective in polar regions. In this paper, from the perspective of global inertial navigation, a novel alignment algorithm based on pseudo-Earth frame and backward process is proposed to implement the initial alignment in polar regions. Considering that an accurate coarse alignment of azimuth is difficult to obtain in polar regions, the dynamic error modeling with large azimuth misalignment angle is designed. At the end of alignment phase, the strapdown attitude matrix relative to local geographic frame is obtained without influence of position errors and cumbersome computation. As a result, it would be more convenient to access the following polar navigation system. Then, it is also expected to unify the polar alignment algorithm as much as possible, thereby further unifying the form of external reference information. Finally, semi-physical static simulation and in-motion tests with large azimuth misalignment angle assisted by unscented Kalman filter (UKF) validate the effectiveness of the proposed method.

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.

A novel gyrocompass alignment method under large azimuth misalignment angle

Conventional gyrocompass alignment methods are based on relatively small azimuth misalignment angles. However, a marine strapdown inertial navigation system may face large azimuth misalignment angle caused by a failed coarse alignment algorithm. This paper provides a novel gyrocompass alignment method to solve the problem. Effects of system parameters are analyzed and the proper scenario of parameter switch based on the classic control theories is derived. Test results show that compared with the conventional methods, our method can accomplish the initial alignment quickly and accurately under large azimuth misalignment angle.

Time-varying gyrocompass alignment for fiber-optic-gyro inertial navigation system with large misalignment angle

Conventional strapdown gyrocompass alignment methods are based on the assumption that the fiber-optic-gyro inertial navigation system has a small azimuth misalignment angle. A large azimuth misalignment angle would lead to an extension of the alignment duration. A time-varying gyrocompass alignment method to solve this problem is provided. An appropriate parameter setting is given for the gyrocompass alignment with a large misalignment angle. Also, a proper protocol for a parametric switch is derived. Simulation and trail results show that the proposed method has better alignment performance than conventional ones, as the system has large misalignment angles.

The Application of Adaptive Square Root Cubature Particle Filtering Algorithm in Initial Alignment of Large Azimuth Misalignment in SINS

The error model of Strap-down Inertial Navigation System (SINS) initial alignment is nonlinear under large azimuth misalignment angle condition, and it could be processed by the Particle Filter (PF) algorithm. Because the importance density function of the standard particle filter algorithm is difficult to select, a new algorithm of the Cubature Particle Filter (CPF) is proposed in this paper. In the new algorithm, the importance density function of standard particle filter is obtained by Adaptive Square root Cubature Kalman Filter (ASCKF), square root decomposition is chosen to enhance the numerical robustness and ensure that the state covariance matrix is positive definite. To meet the realtime requirement, the adaptive factor is introduced to control system model error. As the computer simulation results are shown that this algorithm could reduce the effect of inaccurate noise statistical model and it is a very effective nonlinear filtering algorithm.

Application of ISRCDKF in SINS Initial Alignment with Large Azimuth Misalignment Angle

Application of ISRCDKF in SINS Initial Alignment with Large Azimuth Misalignment Angle

Application of Robust Adaptive CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Application of Robust Adaptive CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle