Alignment Of Inertial Navigation System Research Articles

Theory and practice of hull dynamic deformation law and heading angle transfer error correction

In order to meet the demand of high-precision heading angle transmission in the transfer alignment of inertial navigation system on moving base, the analytical function relationship between the hull deformation and the turning angular velocity and angular acceleration was derived by using the classical beam theory based on the analysis of the equivalent load exerted by the hydrodynamic force and inertia force on the hull structure during the turning process under the combined action of the steering rudder moment and wave force. The objective law between the angular motion and the azimuth deformation angle of the hull under the combined action of maneuvering and sea waves was revealed. Finally, the correction coefficients were determined according to the left turn and right turn motions of the hull by using the measured data of the ship in the sea trial during the S-shape maneuvering navigation, and the azimuth deformation angle correction was completed. The results indicated that the application of the Qu's bending deformation correction formula could greatly reduce the influence of the hull flexural deformation on the heading angle accuracy, meet the needs of high-precision heading angle transmission, and fully verify the correctness of the hull azimuth deformation law and the heading angle transmission error correction theory. This theory and method provided technical support for establishing high-precision distributed digital reference in the field of transfer alignment of inertial navigation on moving base and the application of heading angle transfer of other shipborne equipment.

Design of a new algorithm to improve the convergence of extended Kalman filter based on incremental predictive model for inertial navigation system alignment and its stability analysis

Design of a new algorithm to improve the convergence of extended Kalman filter based on incremental predictive model for inertial navigation system alignment and its stability analysis

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.

Fast initial alignment for inertial navigation system based on high order sliding mode observer and Kalman filter

The inertial navigation system is a dead reckoning system, thus initial alignment for an inertial navigation system plays an important role in the accuracy of it. In this paper, a novel approach for initial alignment in an inertial navigation system with increased speed and accuracy is proposed. This method has two stages, which integrates the Kalman filter and a high order sliding mode observer. In the inertial navigation system, leveling misalignment angles reach the steady-state faster than the azimuth misalignment angle does, which means the azimuth alignment takes a considerable time for initial alignment. Therefore, in this paper at the first stage estimations of state variables of the system are obtained using the Kalman filter and whenever all variables (except azimuth alignment) reach steady-state, the second stage begins. In the second stage, the estimation which is obtained by the Kalman filter is used as the input to design an equivalent system with unknown inputs for inertial navigation system. A high-order sliding mode observer is then used to estimate the states of a system with an unknown input for estimating the azimuth alignment angle. This method not only increases the speed of estimation but also has comparable accuracy.

In-flight inertial navigation system alignment under non-controlled delays in aiding data

In the presented paper we consider a well-known in-flight alignment problem for strapdown inertial navigation system (INS) via position and velocity data provided by the reference inertial navigation system. Main challenges of the mentioned problem are displacement of the reference navigation system relative to the navigation system being aligned, mutual misalignment of the relevant body frames, the presence of uncontrolled time delays in the positional and velocity aiding data. We propose in-flight alignment algorithm that was tested based on simulation. Typical vehicle’s trajectories were derived from relevant telemetry data. The results of numerical experiments are also presented.

Cooperative Localization of a GPS-Denied UAV Using Direction-of-Arrival Measurements

A GPS-denied unmanned aerial vehicle (UAV) (Agent B) is localized through inertial navigation system alignment with the aid of a nearby GPS-equipped UAV (Agent A), which broadcasts its position at several time instants. Agent B measures the signals’ direction of arrival with respect to Agent B's inertial navigation frame. Semidefinite programming and the orthogonal Procrustes algorithm are employed, and accuracy is improved through maximum likelihood estimation. The method is validated using flight data and simulations. A three-agent extension is explored.

Incremental predictive Kalman filter for alignment of inertial navigation system

In this paper, a new filter called incremental predictive Kalman filter is developed and employed for the alignment of inertial navigation system using zero velocity updates method. Utilizing the i...

Application of Improved 5th-Cubature Kalman Filter in Initial Strapdown Inertial Navigation System Alignment for Large Misalignment Angles

In view of the fact the accuracy of the third-degree Cubature Kalman Filter (CKF) used for initial alignment under large misalignment angle conditions is insufficient, an improved fifth-degree CKF algorithm is proposed in this paper. In order to make full use of the innovation on filtering, the innovation covariance matrix is calculated recursively by an innovative sequence with an exponent fading factor. Then a new adaptive error covariance matrix scaling algorithm is proposed. The Singular Value Decomposition (SVD) method is used for improving the numerical stability of the fifth-degree CKF in this paper. In order to avoid the overshoot caused by excessive scaling of error covariance matrix during the convergence stage, the scaling scheme is terminated when the gradient of azimuth reaches the maximum. The experimental results show that the improved algorithm has better alignment accuracy with large misalignment angles than the traditional algorithm.

Anti-disturbance fault tolerant initial alignment for inertial navigation system subjected to multiple disturbances

Modeling error, stochastic error of inertial sensor, measurement noise and environmental disturbance affect the accuracy of an inertial navigation system (INS). In addition, some unpredictable factors, such as system fault, directly affect the reliability of INSs. This paper proposes a new anti-disturbance fault tolerant alignment approach for a class of INSs subjected to multiple disturbances and system faults. Based on modeling and error analysis, stochastic error of inertial sensor, measurement noise, modeling error and environmental disturbance are formulated into different types of disturbances described by a Markov stochastic process, Gaussian noise and a norm-bounded variable, respectively. In order to improve the accuracy and reliability of an INS, an anti-disturbance fault tolerant filter is designed. Then, a mixed dissipative/guarantee cost performance is applied to attenuate the norm-bounded disturbance and to optimize the estimation error. Slack variables and dissipativeness are introduced to reduce the conservatism of the proposed approach. Finally, compared with the unscented Kalman filter (UKF), simulation results for self-alignment of an INS are provided based on experimental data. It can be shown that the proposed method has an enhanced disturbance rejection and attenuation performance with high reliability.

IMU based deformation estimation about the deck of large ship

Accurate attitude information needed by the shipboard equipment is greatly influenced by the deformation of the deck. To solve this problem, some inertial measurement unit (IMU), which is composed by gyros and accelerators, are installed in the key battle points on the deck. The displacement of the key battle point will be measured by the IMU. Inspired by the rapid transfer alignment of inertial navigation system presented by the American scholar Kain, a novel deformation estimation Kalman filter is designed. The measurement model is designed by matching the output of the IMU and the main inertial navigation system of the ship. Then the Kalman filter is applied to estimate the accurate deformation of the key battle point online. The performance of the Kalman filter is influenced by the observability of the system. Observability analysis method named PWCS (piece-wise constant system) is applied for this new presented method. The efficiency of the presented method is proved by theoretical analysis and computer simulation. Simulation results also show that the presented method can estimate the deformation online successfully.

Application ofH∞Filter on the Angular Rate Matching in the Transfer Alignment

The transfer alignment (TA) scheme is used for the initial alignment of Inertial Navigation System (INS) on dynamical base. The Kalman filter is often used in TA to improve the precision of TA. And the statistical characteristics of interference signal which is difficult to get must be known before the Kalman filter is used in the TA, because the interference signal is a random signal and there are some changes on the dynamic model of system. In this paper, theH∞filter is adopted in the TA scheme of the angular rate matching when the various stages of disturbance in measurement are unknown. And it is compared with the Kalman filter in the same environment of simulation and evaluation. The result of simulation shows that theH∞filter and the Kalman filter are both effective. The Kalman filter is more accurate than theH∞filter when system noise and measurement noise are white noise, but theH∞filter is more accurate and quicker than the Kalman filter when system noise and measurement noise are color noise. In the engineering practice, system noise and measurement noise are always color noise, so theH∞filter is more suitable for engineering practice than the Kalman filter.

Static alignment of inertial navigation systems using an adaptive multiple fading factors Kalman filter

Kalman filter is not an optimal estimation method for the systems without any exact model. Therefore, a multiple fading factors matrix has been used as a multiplier for the covariance matrices in these systems. In this paper, a novel method, named adaptive multiple fading factor Kalman filter, is proposed for the systems without initial alignment of strap down inertial navigation systems. By applying this algorithm to different channels of Kalman filter, different coefficients of fading factors are computed. The simulation results show a noticeable increment in alignment's precision and alignment's speed, and a noticeable decrement in sensitivity to unknown noises.

Strapdown inertial navigation system alignment based on marginalised unscented Kalman filter

This study concerns the strapdown inertial navigation system (SINS) initial alignment under marine mooring condition with large initial error. The ten-dimensional state initial alignment error functions of the SINS with inclusion of non-linear characteristics have been derived. It is pointed out for the first time that the non-linear functions are applied to only a subset of the elements of the state vector, that is, the velocities error and the misalignment angles. Then a computationally efficient refinement of the unscented transformation (UT) called marginalised UT (MUT) is investigated in these special non-linear systems with a linear substructure. A performance comparison between the extended Kalman filter (EKF), the UT-based Kalman filter (UKF) and the MUT-based Kalman filter (MUKF) demonstrates that both the UKF and the MUKF can outperform the EKF and the MUKF and can achieve, if not better, at least a comparable performance to the UKF, at a significantly lower expense.

Map Matching-Aided In-Motion SINS Initial Alignment

The paper introduces a novel in-motion SINS initial alignment algorithm using the inertial frame as computing reference. First, dead reckoning method is utilized with the help of the external odometer speed information aiming to eliminate the angular and linear moving disturbances. Second, in order to suppressing the divergence of the positioning error, a new integrated Map Matching algorithm is introduced which is able to reduce the effect of misalignment angle from coarse alignment for positioning error. Road test results show that this algorithm has greatly reduced the coarse alignment of directional accuracy and saved the requirements of speed and route for land vehicles. Not only is the mobility of land vehicle enhanced but also the accuracy of initial alignment of inertial navigation system is improved, which is meaningful in real application.

Initial alignment for nonlinear inertial navigation systems with multiple disturbances based on enhanced anti-disturbance filtering

Initial alignment for inertial navigation system (INS) has been widely used in practice under the assumption of Gaussian noises. In most previous works, nonlinear dynamics was ignored and the disturbances were merged into either a Gaussian or norm-bounded variable, where the Kalman filtering or robust filtering can be applied, respectively. In this article, the unmodelled nonlinear dynamics, drifts, parametric uncertainties, as well as other disturbances are considered simultaneously and are formulated into different types of uncertain disturbances described by the exo-system, stochastic and norm-bounded variable, respectively. A nonlinear initial alignment approach for INS is first presented based on a new disturbance attenuation and rejection filtering scheme against multiple disturbances. The INS error model with both nonlinear dynamics and multiple disturbances is established and the initial alignment problem is transformed into a robust nonlinear filter design problem for a class of nonlinear systems with multiple disturbances. In the proposed composite filtering approach, the drift filter is designed to estimate and compensate the inertial sensor drift. Mixed H 2/H ∞ filtering is designed to optimise the estimation error and attenuate the norm-bounded uncertain disturbances, respectively. Simulations for ground stationary base initial alignment of an INS are provided. Comparisons show that the concerned INS has the enhanced disturbance rejection and attenuation performance.

Processing algorithm for a strapdown gyrocompass

The problem of gyrocompassing using inertial sensors, i.e., gyros and accelerometers, is addressed. North finding, with an order of accuracy of one arc-min, is not only required for the initial alignment of inertial navigation systems, but also has a critical role to play in the guidance and navigation of ships that navigate for long periods of time. In this work, after extracting the error model of an inertial navigation system and augmenting it with the error model of inertial sensors, a processing algorithm based on the Kalman filter is designed and simulated to process the navigation system velocity error, and to estimate and correct tilt and heading errors along with gyro drifts and accelerometer biases. It is verified that using gyros with drift stability of 0.01 deg/hr, and accelerometers with bias stability of 100 μg, the true north direction can be determined with an accuracy of about five arc-mins.

Multi-objective robust initial alignment algorithm for Inertial Navigation System with multiple disturbances

In this paper, the problem of initial alignment for Inertial Navigation System (INS) is discussed. Based on the Lyapunov transformation, INS error model of stationary base is established. Then, the initial alignment for INS can be transformed into the robust filter design problem for linear systems with multiple disturbances. A robust multi-objective filter is constructed for the concerned INS with disturbance rejection and attenuation performance, where the drift estimations are applied to reject the inertial sensor drifts and mixed H2/H∞ filter is adopted to attenuate Gaussian noises and norm bounded disturbances. Finally, simulations for stationary base alignment of an Inertial Navigation System are given to show the efficiency of the proposed approach.

A consistent and robust Kalman filter design for in-motion alignment of inertial navigation system

The necessity recurrently comes up to align a strapdown inertial navigation system (SINS) in a moving vehicle to avoid a long run-up of the inertial system before a start or launch command is issued. This in-motion alignment is therefore achieved by integrating SINS data with some external aiding source such as the Global Positioning System (GPS) by using some form of measurement matching method. Consequently, this paper illustrates a reliable in-motion alignment scheme for a low-cost strapdown inertial measurement unit (SIMU) using a consistent and robust Kalman filter (RKF) structure. An error model of the SINS is derived and the state vector comprises attitude, velocity, position and sensor errors. Velocity information from the GPS with maneuvering is employed as a measurement to the filter. Experimentation results show that the proposed filter is less sensitive to impulsive noise and gives better estimates of the navigation parameters.

Comparison of robust H∞ filter and Kalman filter for initial alignment of inertial navigation system

There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system. This paper discussed the use of GPS, but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS). One method is based on the Kalman filter (KF), and the other is based on the robust filter. Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF, given substantial process noise or unknown noise statistics. So the robust filter is an effective and useful method for initial alignment of SINS. This research should make the use of SINS more popular, and is also a step for further research.

Nonlinear filtering methods application in INS alignment

The problem of course alignment of inertial navigation systems is considered. Error equations of nonlinear character have been obtained at a considerable level of a priori course uncertainty. The algorithm for solving the problem of optimal estimation of the state vector described by nonlinear equations from linear measurements has been developed for the analysis of a potential alignment accuracy with the piecewise-Gaussian approximation of a posteriori density used. The results of the study of efficiency of various suboptimal alignment algorithms obtained by the use of the software developed are presented and discussed.