Transfer Alignment Algorithm Research Articles

Comparison of Filtering Techniques for Transfer Alignment of Air Launched Tactical Guided Weapons

The transfer alignment technique is very useful for the accurate initialization and calibration of gyroscopes and accelerometers of INS for air-launched tactical guided weapon systems. In the present war scenario, the initialization of INS should be accurate and rapid to launch the tactical weapon against air or land targets within the shortest available time. A lot of development has been carried out by researchers for INS transfer alignment in the field of state estimation. The study and method presented in the research are relevant to aerial launch vehicles. However, to meet present guidance requirements within less time for initializing weapons, more appropriate transfer alignment algorithms are needed. This paper discusses the relative performance of Kalman Filter (KF), Extended KF, and Unscented KF for aligning the weapon INS using the data from Master INS. New developments, limitations, applicability, and design methods for the Kalman filters, became a key component in the transfer alignment of air-launched tactical missiles. These methodologies are extensively utilized in Navigation and Control systems; therefore, this research work will be an informative and perfect guide to existing and potential readers.

Design of Transfer Alignment Algorithm with Velocity and Azimuth Matching for the Aircraft Having Wing Flexibility

A transfer alignment is used to initialize, align, and calibrate a SINS(Slave INS) using a MINS(Master INS) in motion. This paper presents an airborne transfer alignment with velocity and azimuth matching to estimate inertial sensor biases under the wing flexure influence. This study also considers the lever arm, time delay and relative orientation between MINS and SINS. The traditional transfer alignment only uses velocity matching. In contrast, this paper utilizes the azimuth matching to prevent divergence of the azimuth when the aircraft is stationary or quasi-stationary since the azimuth is less affected by the wing flexibility. The performance of the proposed Kalman filter is analyzed using two factors; one is the estimation performance of gyroscope and accelerometer bias and the other is comparing aircraft dynamics and attitude covariance. The performance of the proposed filter is verified using a long term flight test. The test results show that the proposed scheme can be effectively applied to various platforms that require airborne transfer alignment.

A SE(2)-based transfer alignment for large installation misalignment angle

Transfer alignment is the optimal solution for initial alignment of shipborne or airborne weapons. The need for transfer alignment is increasing at large installation misalignment angles. Traditional linear models have large attitude errors at large installation misalignment angles, nonlinear algorithms are computationally intensive and difficult to be widely used, and quaternion optimization algorithms are sensitive to noise. In this paper, the Lie group theory is introduced for the first time in the study of transfer alignment. The attitude and velocity are represented as the Group of Direct Planar Isometries (SE(2)), and then the SE(2)-based error vectors are constructed by log-linearization. The SE(2)-based linear transfer alignment algorithm is developed for large installation misalignment conditions. The computational effort of the proposed algorithm is very small due to its linear structure, and it has strong engineering application value. The simulation and field test are conducted to illustrate the effectiveness of the proposed transfer alignment algorithm.

A transfer alignment algorithm based on combined double-time observation of velocity and attitude

PurposeThis paper aims to present a novel transfer alignment method based on combined double-time observations with velocity and attitude for ships’ poor maneuverability to address the system errors introduced by flexural deformation and installing which are difficult to calibrate.Design/methodology/approachBased on velocity and attitude matching, redesigning and deducing Kalman filter model by combining double-time observation. By introducing the sampling of the previous update cycle of the strapdown inertial navigation system (SINS), current observation subtracts previous observation are used as measurements for transfer alignment filter, system error in measurement introduced by deformation and installing can be effectively removed.FindingsThe results of simulations and turntable tests show that when there is a system error, the proposed method can improve alignment accuracy, shorten the alignment process and not require any active maneuvers or additional sensor equipment.Originality/valueCalibrating those deformations and installing errors during transfer alignment need special maneuvers along different axes, which is difficult to fulfill for ships’ poor maneuverability. Without additional sensor equipment and active maneuvers, the system errors in attitude measurement can be eliminated by the proposed algorithms, meanwhile improving the accuracy of the shipboard SINS transfer alignment.

A Transfer Alignment Algorithm Based on Combined Double-Time Observation of Velocity and Attitude

A Transfer Alignment Algorithm Based on Combined Double-Time Observation of Velocity and Attitude

A transfer alignment method for inertial attitude correction of spherical motor on moving base

Inertial attitude measurement of spherical motor on moving base (SMMB) confronts the problems of unobservable azimuth and acceleration interference. This paper presents a transfer alignment method for inertial attitude correction of SMMB with specific exterior excitation. Because of the low friction torque of the spherical joint in the SMMB, the rotation is not able to be wholly transferred from base to rotor, which is required in the common method of velocity-attitude matching. With an explicit model about the acceleration transfer, the accumulation of unobservable error based on nominal-state is derived. Then the transfer alignment algorithm with innovation-based adaptive filter is proposed. The method brings external acceleration measurement into regular attitude estimation and adopts both estimation of unobservable error and acceleration transfer error into adaptive filter for the balance of azimuth correction and tilt error suppression. The simulation and analysis show that the method can correct the unobservable azimuth angle in high amplitude and frequency excitation as well as better tilt error suppression for transfer error with 20% uncertainty. The method is verified in a prototype platform.

An Anti-Wing Flexure Velocity+Attitude Matching Rapid Transfer Alignment Algorithm

An Anti-Wing Flexure Velocity+Attitude Matching Rapid Transfer Alignment Algorithm

Strapdown Inertial Navigation System Transfer Alignment: Algorithmic Features and Simulation Performance Analysis

The way of small aircraft strapdown inertial navigation system transfer alignment based on information from the carrier navigation system is considered. Optimal Kalman filter was selected as the main mathematical technique for the proposed method implementation, which showed acceptable accuracy and convergence time in the set task conditions. The main features of SINS transfer alignment algorithm and the results of its operation at complex trajectory simulation are presented.

Design of a fast transfer alignment matching algorithm

In order to estimate the misalignment angle of align system effectively, this paper constructs a traditional 14 state rapid transfer alignment model, considers the problem that traditional velocity error definition is not completely accord with the consistency of coordinate system, the improved differential equation of velocity error is applied to transfer alignment, constructs an improved 12 state rapid transfer alignment model. Finally, the performance of the two alignment algorithms is tested by vehicle-mounted experimental data under the conditions of no maneuver, C shape maneuver and S shape maneuver, and the experimental result of the two algorithms is compared and analyzed. The experimental result shows that the improved 12 state rapid transfer alignment algorithm not only has better alignment accuracy than the traditional 14 state rapid transfer alignment algorithm, but also reduces the model complexity, reduces the system calculation, and avoids the influence of flexural deflection and dynamic misalignment angle. The yaw angle alignment accuracy is improved by 45%, 65% and 53% under the conditions of no maneuver, C shape maneuver and S shape maneuver, respectively.

The transfer alignment method based on the inertial network

The purpose of this paper is to propose a new transfer alignment approach based on the inertial network. The inertial network is described, and the measurement models are built firstly. The inertial device fusion and fault detection algorithm are analyzed with the redundancy technology of devices in the inertial network to enhance the reliability and security of the system. Then the improved model of the system is built and the transfer alignment algorithm method for the inertial network is proposed. Several experiments have been performed to validate the performance of the proposed transfer alignment scheme. Different simulation experiments and real ground vehicle test results showed that the transfer alignment with the inertial network can obtain the better precision result and shorter convergence time, which can also improve the fault detection and recognition ability of the system. A transfer alignment method based on the inertial network is proposed to improve the fault tolerance ability and enhance the accuracy of the system. The primary advantage is that each node is able to communicate with other nodes, which allows for sharing information in the inertial network.

An Improved Real-Time Transfer Alignment Algorithm Based on Adaptive Noise Estimation for Distributed POS

Distributed position and orientation system (POS) plays an important role in the fields of aerial remote sensing, which serves the sensors by precise motion information. For distributed POS, the slave systems consist of low accuracy inertial sensors, which must depend on the high accuracy motion information of the master to proceed transfer alignment (TA) to improve accuracy. Generally, the TA filtering algorithms perform superior performance when the noise statistical characteristic is known and accurate, however like gust, engine vibration and other external disturbances both will cause the inertial sensors output with unknown, varying noises and performance decline. Aiming at this, a variational Bayesian central difference Kalman filtering algorithm for distributed POS real-time TA is developed to suppress the effect of external noise, incorporating the central difference Kalman filtering (CDKF) algorithm and variational Bayesian (VB) adaption theory. In detail, the algorithm is to apply the noise estimation by VB adaption to the CDKF algorithm to achieve real-time TA accuracy enhancement. Distributed POS flight test is devoted for the algorithm validation, by the comparison, evident progress in TA accuracy has been presented.

쿼터니언 정합 기반 전달정렬 필터

Transfer alignment, which has been widely researched, is the main process of finding the attitude information of a slave inertial navigation system by using navigation information of a master inertial navigation system (MINS). Transfer alignment algorithms can be classified according to the information of the MINS, such as velocity matching, attitude matching, angular rate matching, and acceleration matching. In this paper, we only consider the attitude matching algorithm and propose a new transfer alignment algorithm based on quaternion information. Moreover, we prove that the proposed algorithm is equivalent to the transfer alignment algorithm based on direction cosine matrix information, which has been used for attitude matching.

A Novel Robust Sparse-Grid Quadrature Kalman Filter Design for HCV Transfer Alignment Against Model Parameter Uncertainty

A novel robust scheme for Transfer Alignment (TA) is proposed for improving the accuracy of the navigation of a Hypersonic Cruise Vehicle (HCV). The main goal is to instil robustness in the safety and accuracy of the attitude determination, despite mode uncertainties. This article focuses on Robust Sparse-Grid Quadrature Filtering (R-SGQF) using two given robust factors for norm-bounded model uncertainties in non-linear systems. Missile dynamic and measurement model uncertainties are established to validate TA technologies. The nominal stability of the R-SGQF is defined by estimating error dynamics. The technique gives sufficient conditions for the R-SGQF in terms of two parameterised Riccati equations. Robust stability is analysed using Lyapunov theory and the accuracy level of the Sparse-Grid Quadrature (SGQ) algorithm. Embedding the SGQ technique into the robust filter structure, R-SGQF is not only of robust stability against uncertainty but also of higher accuracy. The simulation results of the TA algorithm demonstrate that attitude determinations validate the effectiveness of the R-SGQF algorithm.

A New Polar Transfer Alignment Algorithm with the Aid of a Star Sensor and Based on an Adaptive Unscented Kalman Filter.

Because of the harsh polar environment, the master strapdown inertial navigation system (SINS) has low accuracy and the system model information becomes abnormal. In this case, existing polar transfer alignment (TA) algorithms which use the measurement information provided by master SINS would lose their effectiveness. In this paper, a new polar TA algorithm with the aid of a star sensor and based on an adaptive unscented Kalman filter (AUKF) is proposed to deal with the problems. Since the measurement information provided by master SINS is inaccurate, the accurate information provided by the star sensor is chosen as the measurement. With the compensation of lever-arm effect and the model of star sensor, the nonlinear navigation equations are derived. Combined with the attitude matching method, the filter models for polar TA are designed. An AUKF is introduced to solve the abnormal information of system model. Then, the AUKF is used to estimate the states of TA. Results have demonstrated that the performance of the new polar TA algorithm is better than the state-of-the-art polar TA algorithms. Therefore, the new polar TA algorithm proposed in this paper is effectively to ensure and improve the accuracy of TA in the harsh polar environment.

Modified compensation algorithm of lever-arm effect and flexural deformation for polar shipborne transfer alignment based on improved adaptive Kalman filter

Due to the lever-arm effect and flexural deformation in the practical application of transfer alignment (TA), the TA performance is decreased. The existing polar TA algorithm only compensates a fixed lever-arm without considering the dynamic lever-arm caused by flexural deformation; traditional non-polar TA algorithms also have some limitations. Thus, the performance of existing compensation algorithms is unsatisfactory. In this paper, a modified compensation algorithm of the lever-arm effect and flexural deformation is proposed to promote the accuracy and speed of the polar TA. On the basis of a dynamic lever-arm model and a noise compensation method for flexural deformation, polar TA equations are derived in grid frames. Based on the velocity-plus-attitude matching method, the filter models of polar TA are designed. An adaptive Kalman filter (AKF) is improved to promote the robustness and accuracy of the system, and then applied to the estimation of the misalignment angles. Simulation and experiment results have demonstrated that the modified compensation algorithm based on the improved AKF for polar TA can effectively compensate the lever-arm effect and flexural deformation, and then improve the accuracy and speed of TA in the polar region.

Utilizing Time Redundancy for Particle Filter-Based Transfer Alignment

Signal detection in the presence of high noise is a challenge in natural sciences. From understanding signals emanating out of deep space probes to signals in protein interactions for systems biology, domain specific innovations are needed. The present work is in the domain of transfer alignment (TA), which deals with estimation of the misalignment of deliverable daughter munitions with respect to that of the delivering mother platform. In this domain, the design of noise filtering scheme has to consider a time varying and nonlinear system dynamics at play. The accuracy of conventional particle filter formulation suffers due to deviations from modeled system dynamics. An evolutionary particle filter can overcome this problem by evolving multiple system models through few support points per particle. However, this variant has even higher time complexity for real-time execution. As a result, measurement update gets deferred and the estimation accuracy is compromised. By running these filter algorithms on multiple processors, the execution time can be reduced, to allow frequent measurement updates. Such scheme ensures better system identification so that performance improves in case of simultaneous ejection of multiple daughters and also results in better convergence of TA algorithms for single daughter.

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.

Study on a Twice Transfer Alignment Based on Dual Model

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

Rapid transfer alignment of laser SINS using quaternion based angular measurement

A novel rapid transfer alignment algorithm for laser strapdown inertial navigation system (SINS) is studied. Transfer alignment have typically relied on velocity measurements from the master SINS as the source of alignment information, but lever arm error must be compensated accurately while velocity information is utilized, all most every quaternion based error model is nonlinear, so nonlinear filtering algorithms are need, suffering from computational complex and large error. Aim at these problems, a novel improved rapid transfer alignment algorithm formulation is presented, applying quaternion to built the process and measurement models, the improvement employs a special manipulation of the measurement equation results in a linear pseudo-measurement equation, thus the classical linear Kalman filter is employed to estimate the state, need not lever arm error compensation, results in the reduce of computational burden. Observability analysis of this new transfer alignment algorithm has been done based on the piece-wise constant system (PWCS) method, results show that the presented algorithm can accomplish the initial alignment task perfectly. A transfer alignment simulation system is also developed for the evaluation and analysis of the presented algorithm, simulation results are confirmed with the theoretical conclusion, which can achieve the transfer alignment accuracy about 1mrad within 10s.

Comparison of Three Rapid Transfer Alignment Algorithms

Speed of transfer alignment affects the ability of rapid response of the weapons equipped on the ship or the plane, so rapid transfer alignment has been widely researched for the last two decades. The “velocity plus attitude” matching rapid transfer alignment algorithm which is presented by the American scientist Kain, who started the times of rapid transfer alignment, then lots of researches have been done based on Kain’s work, every one has its own advantages, and of course disadvantages. Three rapid transfer alignment algorithms called “velocity plus attitude”, “velocity plus rate” and “attitude plus rate” matching rapid transfer alignment, have been compared from the aspect of accuracy and speed in this paper, while the flexure of the vehicle and the lever arm errors occurred respectively. Simulations have been done for the comparison of these three rapid transfer alignment algorithms, results show that while there is no flexure nor lever arm error, the three methods perform the same in accuracy and speed, the “velocity plus attitude” is a little slower than the other two methods in the estimation of the misalignment, the flexure influence the “velocity plus rate” and “attitude plus rate” matching methods much more than the “velocity plus attitude” method, but the latter greatly affected by the lever arm error. The results of this paper can help engineers to chose better rapid transfer alignment algorithm in their work.