Large Misalignment Angle Research Articles

INITIAL ALIGNMENT OF FIBER-OPTIC INERTIAL NAVIGATION SYSTEM WITH LARGE MISALIGNMENT ANGLES BASED ON GENERALIZED PROPORTIONAL-INTEGRAL-DERIVATIVE FILTER

Abstract Initial alignment in the presence of large misalignment angles is a critical issue in strapdown inertial navigation systems. The large initial misalignment angle adversely affects the accuracy and rapidness of the alignment process. In this paper a novel robust alignment approach is proposed based on a generalized proportional-integral-derivative filter. The proposed alignment approach has some significant advantages compared to the standard Kalman filter based alignment method. This method increases the accuracy and the convergence speed of the alignment process in the large misalignment angles problem. Experimental results also, verify the prominent performance of the presented approach in comparison to conventional standard Kalman filter based alignment method.

Novel SINS Initial Alignment Method under Large Misalignment Angles and Uncertain Noise Based on Nonlinear Filter

For the SINS initial alignment problem under large misalignment angles and uncertain noise, two novel nonlinear filters, referred to as transformed unscented quadrature Kalman filter (TUQKF) and robust transformed unscented quadrature Kalman filter (RTUQKF), are proposed in this paper, respectively. The TUQKF sets new deterministic sigma points to address the nonlocal sampling problem and improve the numerical accuracy. The RTUQKF is the combination ofH∞technique and TUQKF. It improves the accuracy and robustness of state estimation. Simulation results indicate that TUQKF performs better than traditional filters when misalignment angles are large. Turntable and vehicle experiments results indicate that, under the condition of uncertain noise, the performances of RTUQKF are better than other filters and more robust. These two methods can effectively further increase precision and convergence speed of SINS initial alignment.

Robust H‐infinity CKF/KF hybrid filtering method for SINS alignment

This study concerns the in-motion alignment in the strapdown inertial navigation system (SINS) with large misalignment angles. As the non-linear filtering method applied in the alignment model is quite computer intensive, which has a significant impact on the alignment accuracy and speed. To solve this problem, a robust H-infinity cubature Kalman filter (CKF)/KF hybrid filter (RHCHF) is proposed to lower the computational burden and strengthen the robustness. By virtue of the idea of model decomposition, the RHCHF could estimate the non-linear and linear parts of alignment model, respectively. Through the introduction of robust factor to adjust the filter parameters, it can ensure the accuracy reliably. The comparisons of the simulation and vehicle experiment demonstrate that the RHCHF could achieve the results at a significantly lower expense than the unscented Kalman filter, and obtain a high accuracy even when the statistical property of noise is uncertain or the outliers of measurement occur occasionally.

Fuzzy adaptive strong tracking scaled unscented Kalman filter for initial alignment of large misalignment angles.

In the initial alignment process of strapdown inertial navigation system (SINS), large misalignment angles always bring nonlinear problem, which can usually be processed using the scaled unscented Kalman filter (SUKF). In this paper, the problem of large misalignment angles in SINS alignment is further investigated, and the strong tracking scaled unscented Kalman filter (STSUKF) is proposed with fixed parameters to improve convergence speed, while these parameters are artificially constructed and uncertain in real application. To further improve the alignment stability and reduce the parameters selection, this paper proposes a fuzzy adaptive strategy combined with STSUKF (FUZZY-STSUKF). As a result, initial alignment scheme of large misalignment angles based on FUZZY-STSUKF is designed and verified by simulations and turntable experiment. The results show that the scheme improves the accuracy and convergence speed of SINS initial alignment compared with those based on SUKF and STSUKF.

Applied Quaternion Optimization Method in Transfer Alignment for Airborne AHRS Under Large Misalignment Angle

The airborne attitude and heading reference system (AHRS) is an inertial system that provides the aircraft with navigation parameters in real time. A new coarse transfer alignment method is proposed for low-accuracy AHRS with a large misalignment angle between AHRS and master inertial navigation system (MINS). The new coarse alignment method is derived from the quaternion-optimization-based in-flight alignment (IFA) method. By resolving the alignment problem in body frame, the proposed method makes full use of the navigation information of MINS and transforms the alignment problem into a quaternion optimization problem. Its accuracy is improved by presenting a two-step iterative algorithm. Compared with the quaternion-optimization-based IFA method, the proposed method is more robust and accurate by introducing the attitude information of MINS. The simulation and experiment results show that the proposed method is applicable to both large and small misalignment angles, and it serves as a nice coarse alignment that permits the implementation of linear error model in fine alignment process.

Polar transfer alignment of shipborne SINS with a large misalignment angle

Existing polar transfer alignment (TA) algorithms are designed based on linear Kalman filters (KF) to estimate misalignment angles. In the case of a large misalignment angle, these algorithms cannot be applied in order to achieve accurate TA. In this paper, a TA algorithm based on an unscented Kalman filter (UKF) is proposed to solve the problem of the large misalignment angle in the polar region. Based on a large misalignment angle, nonlinear navigation error equations, which are the UKF dynamic models, are derived under grid frames. This paper chooses the velocity plus attitude matching method as the TA matching method and errors of velocity and attitude as observations. The misalignment angle can be estimated by the designed UKF. The simulation results have demonstrated that the polar TA algorithm can be effective in improving the TA accuracy, especially when large misalignment angles occur.

Integration and Simulations of INS/GNSS System using the Approach of Carrier Phase Measurements

<p class="MsoNormal" style="margin-top: 12.0pt; margin-right: 0in; margin-bottom: 6.0pt; margin-left: 0in; text-align: justify;"><em><span style="font-size: 9.0pt; font-family: "Arial",sans-serif; mso-ascii-theme-font: minor-bidi; mso-hansi-theme-font: minor-bidi; mso-bidi-theme-font: minor-bidi;" lang="EN-GB">This paper discusses the techniques of attitude, velocity ad position estimation from GNSS carrier phase measurements, and investigates the performance of the lower precision MEMS-based INS/GNSS system based on carrier phase measurements. Double differenced carrier phase measurements provide more accurate velocity and position estimation compared to code and Doppler measurements. However, integer ambiguity is required to be removed for precise positioning. Multiples<span style="color: red;"> </span>antennae approach is used to derive the attitude information from carrier phase measurements in order to control the large initial misalignment angles for initialization of the integration process or to utilize during benign dynamics. Lever arm effect is considered to compensate for the separation of GNSS antenna and IMU location. The derived three GNSS observables are used to correct the INS through optimal Kalman filtering in a closed loop. Simulation results indicates the effectiveness of the integrated system for airborne as well as for land navigation vehicles</span></em><span lang="EN-GB">. </span></p><div id="_mcePaste" class="mcePaste" style="position: absolute; left: -10000px; top: 0px; width: 1px; height: 1px; overflow: hidden;"><p class="MsoNormal" style="margin-top: 12.0pt; margin-right: 0in; margin-bottom: 6.0pt; margin-left: 0in; text-align: justify;"><em><span style="font-size: 9.0pt; font-family: "Arial",sans-serif; mso-ascii-theme-font: minor-bidi; mso-hansi-theme-font: minor-bidi; mso-bidi-theme-font: minor-bidi;" lang="EN-GB">This paper discusses the techniques of attitude, velocity ad position estimation from GNSS carrier phase measurements, and investigates the performance of the lower precision MEMS based INS/GNSS system based on carrier phase measurements. Double differenced carrier phase measurements provide more accurate velocity and position estimation compared to code and Doppler measurements. However, integer ambiguity is required to be removed for precise positioning. Multiples<span style="color: red;"> </span>antennae approach is used to derive the attitude information from carrier phase measurements in order to control the large initial misalignment angles for initialization of the integration process or to utilize during benign dynamics. Lever arm effect is considered to compensate for the separation of GNSS antenna and IMU location. The derived three GNSS observables are used to correct the INS through optimal Kalman filtering in a closed loop. Simulation results indicates the effectiveness of the integrated system for airborne as well as for land navigation vehicles</span></em><span lang="EN-GB">. </span></p></div>

Improving the Performance of INS/GNSS System Using the Approach of Carrier Phase Measurements

This paper discusses the techniques of attitude, velocity ad position estimation from GNSS carrier phase measurements, and investigates the performance of the lower precision MEMS based INS/GNSS system based on carrier phase measurements. Generally, a GPS receiver estimates the position and velocity from code phase and Doppler measurements. Double differenced carrier phase measurements provide more accurate velocity and position estimation compared to code and Doppler measurements. However, for position measurement, the integer ambiguity is required to be removed. Multiples antennae approach is used to derive the attitude information from carrier phase measurements in order to control the large initial misalignment angles for initialization of the integration process or to utilize during benign dynamics. Lever arm effect is considered to compensate for the separation of GNSS antenna and IMU location. The derived three GNSS observables are used to correct the INS through optimal Kalman filtering in a closed loop. Simulation results indicates the effectiveness of the integrated system for airborne as well as for land navigation vehicles.

An Improved Inertial Frame Alignment Algorithm Based on Horizontal Alignment Information for Marine SINS.

In this paper, an improved inertial frame alignment algorithm for a marine SINS under mooring conditions is proposed, which significantly improves accuracy. Since the horizontal alignment is easy to complete, and a characteristic of gravity is that its component in the horizontal plane is zero, we use a clever method to improve the conventional inertial alignment algorithm. Firstly, a large misalignment angle model and a dimensionality reduction Gauss-Hermite filter are employed to establish the fine horizontal reference frame. Based on this, the projection of the gravity in the body inertial coordinate frame can be calculated easily. Then, the initial alignment algorithm is accomplished through an inertial frame alignment algorithm. The simulation and experiment results show that the improved initial alignment algorithm performs better than the conventional inertial alignment algorithm, and meets the accuracy requirements of a medium-accuracy marine SINS.

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.

Three-dimensionally woven glass fiber composite struts: characterization and mechanical response in tension and compression

Three-dimensionally woven E- and S2-glass fiber textiles have been used in the past to create delamination-resistant corrugated core sandwich panels. During subsequent out-of-plane loading, the E-glass composite core struts and S2-glass composite faces are subjected to either compressive or tension loads. This study has investigated the relationships between the three-dimensional fiber architecture, fiber properties and the mechanical response of representative samples of the core and faces. Using X-ray computed tomography and optical microscopy to characterize the three-dimensional fiber architectures, it is found that the in-plane warp and weft fibers suffer significant off-axis displacement (waviness) due to their interaction with through thickness z-fiber tows. The consequence of this fiber waviness on the relationships of the in-plane tensile and compressive mechanical properties, along with fiber type, fiber volume fraction, and strut aspect ratio are experimentally investigated. The large initial misalignment angle of the warp and weft fiber tows results in a strut compressive strength that is substantially lower than its tensile strength due to compressive failure by either elastic or localized fiber microbuckling. Simple micromechanical models are used to relate the compressive strength of the three-dimensional woven composite struts to strut aspect ratio, fiber volume fractions in the three directions and the three-dimensional fiber architecture.

MIMU Parameter Identification for Large Misalignment Angles Based on AFSA

High precision calibration of Micro inertial measurement unit (MIMU) for large installation misalignment can improve the instrument and the practical use of the system precision. It can use high precision turntable to get the coarse value of installation misalignment angle as the optimization initial value, and take the standard deviation of turntable angular rate and the acceleration of gravity as the optimum index to find MIMU misalignment parameters using artificial fish swarm algorithm. Simulation and analysis are carried out based on the position and rate experiment. In addition, static navigation test is taken by using self-developed MEMS heading and attitude measurement system. Both the simulation and experimental results show that this method can improve the calibration accuracy of MIMU effectively with reduced of heading and attitude error.

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.

GIANT OUTBURSTS IN Be/X-RAY BINARIES

Be/X-ray binary systems exhibit both periodic (Type I) X-ray outbursts and giant (Type II) outbursts, whose origin has remained elusive. We suggest that Type II X-ray outbursts occur when a highly misaligned decretion disk around the Be star becomes eccentric, allowing the compact object companion to capture a large amount of material at periastron. Using 3D smoothed particle hydrodynamics simulations we model the long term evolution of a representative Be/X-ray binary system. We find that periodic (Type I) X-ray outbursts occur when the neutron star is close to periastron for all disk inclinations. Type II outbursts occur for large misalignment angles and are associated with eccentricity growth that occurs on a timescale of about 10 orbital periods. Mass capture from the eccentric decretion disk results in an accretion disk around the neutron star whose estimated viscous time is long enough to explain the extended duration of Type II outbursts. Previous studies suggested that the outbursts are caused by a warped disk but our results suggest that this is not sufficient, the disk must be both highly misaligned and eccentric to initiate a Type II accretion event.

New rapid transfer alignment method for SINS of airborne weapon systems

Transfer alignment is an effective alignment method for the strapdown inertial navigation system (SINS) of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer alignment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for arbitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear transfer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibility and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.

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.

Dual-Model Reverse CKF Algorithm in Cooperative Navigation for USV

As one of the most promising research directions, cooperative location with high precision and low-cost IMU is becoming an emerging research topic in many positioning fields. Low-cost MEMS/DVL is a preferred solution for dead-reckoning in multi-USV cooperative network. However, large misalignment angles and large gyro drift coexist in low-cost MEMS that leads to the poor observability. Based on cubature Kalman filter (CKF) algorithm that has access to high accuracy and relative small computation, dual-model filtering scheme is proposed. It divides the whole process into two subsections that cut off the coupling relations and improve the observability of MEMS errors: it first estimates large misalignment angle and then estimates the gyro drift. Furthermore, to improve the convergence speed of large misalignment angle estimated in the first subsection, “time reversion” concept is introduced. It uses a short period time to forward and backward several times to improve convergence speed effectively. Finally, simulation analysis and experimental verification is conducted. Simulation and experimental results show that the algorithm can effectively improve the cooperative navigation performance.

Moving State Marine SINS Initial Alignment Based on High Degree CKF

A new moving state marine initial alignment method of strap-down inertial navigation system (SINS) is proposed based on high-degree cubature Kalman filter (CKF), which can capture higher order Taylor expansion terms of nonlinear alignment model than the existing third-degree CKF, unscented Kalman filter and central difference Kalman filter, and improve the accuracy of initial alignment under large heading misalignment angle condition. Simulation results show the efficiency and advantage of the proposed initial alignment method as compared with existing initial alignment methods for the moving state SINS initial alignment with large heading misalignment angle.

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.