Accuracy Of Transfer Alignment Research Articles

A high-accuracy system model and accuracy evaluation method for transfer alignment

With the increasing demand for high-accuracy navigation, distributed payloads put forward higher requirements for the initial alignment accuracy of their equipped slave inertial navigation systems (INSs). However, the existing transfer alignment models have poor compensation effect on flexible deformation, resulting in low alignment accuracy. Moreover, these models cannot accurately evaluate the accuracy of transfer alignment due to ignoring the errors of the master INS. To address these two problems, a new high-accuracy transfer alignment system model is established by comprehensively considering errors of the master and slave INSs. Further, the propagation characteristics of the master and slave inertial sensor errors in transfer alignment are analyzed, and the relationship expression between the master and slave inertial sensor accuracy and the transfer alignment accuracy is derived. Thus, the accuracy of transfer alignment can be pre-evaluated according to the master and slave inertial sensor accuracy. The experiment results show that the alignment accuracy of the proposed method is improved by more than 71.56% compared with the traditional methods, and the relative errors between the proposed accuracy evaluation method and the Monte Carlo simulations are less than 9.14%. Thus, the correctness, effectiveness and superiority of the proposed method are verified.

Study on the method of transfer alignment based on the distributed inertial network of guided submunition

Aiming at the problem of poor transfer alignment accuracy between missiles with a high length-to-diameter ratio as a carrier missile and the internally carried guided submunition, this study proposes a transfer alignment method based on the distributed inertial network of the guided submunition. Initially, considering the influence of the deflection deformation of the carrier missile body and the dynamic nonlinear deformation of the lever arm on the transfer alignment accuracy, the corresponding error model is established and compensated for the sub-inertial guide velocity to improve the accuracy of the observed quantities. Additionally, in response to the high failure rate of low-cost inertial guidance systems equipped with guided submunition, we used an inertial network data fusion architecture based on the isolation of device failure monitoring, which effectively isolates the faulty data through the fusion of inertial measurements at the sensor level and optimal fusion of filtering to further improve the reliability of transmission alignment. The effectiveness of the method proposed in this paper is verified through a large number of simulation experiments. Compared with the traditional transfer alignment method between a single master inertial guide and a sub inertial guide, the method in the paper has obvious improvement in the transfer alignment accuracy. In addition, this method is not affected by the failure of inertial devices in the distributed inertial network, and can continuously maintain a high transfer alignment accuracy. For this kind of missile-borne distributed inertial network with a sub-node sets layer, it is found after comparison that: the transmission alignment error is basically the same for different slave nodes in the same sub-node sets; between different sub-node sets, the closer to the master inertial guide the transmission alignment error is smaller. This study provides a new solution idea and certain reference value for solving the problem of poor transfer alignment accuracy between missiles with a high length-to-diameter ratio as a carrier missile and the internally carried guided submunition.

Transfer alignment method based on installation deviation angle estimation for array POS

Transfer alignment of array Position and Orientation System (POS) is the key technology to provide motion information for airborne earth observation high-precision imaging. Because the large deviation angle between main Inertial Measurement Unit (IMU) and slave IMU is difficult to accurately measure by existing methods, the transfer alignment accuracy will be seriously affected. Aiming at this issue, a transfer alignment method based on installation deviation angle estimation for array POS is proposed. This method includes two steps: pre-estimation and improved transfer alignment. First, two deviation angles are calculated by angular velocity and acceleration using quaternion optimization method respectively. The higher-precision deviation angle is fused by two deviation angles using their respective weights, which are calculated by Signal to Noise Ratios (SNRs). Second, the result of pre-estimation is used in transfer alignment. Residual errors of deviation angle are considered as state variables and be compensated online. Compared with the existing methods, the velocity, position and attitude errors calculated by this method are reduced by 15.4%, 31.7% and 66.5% respectively. The proposed method significantly reduces the adverse effect of installation deviation angle on measurement accuracy of array POS.

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.

Adaptive transfer alignment method based on the observability analysis for airborne pod strapdown inertial navigation system

For the airborne pod strapdown inertial navigation system, it is necessary to use the host aircraft's inertial navigation system for the transfer alignment as quickly and accurately as possible in the flight process of the aircraft. The purpose of this paper is to propose an adaptive transfer alignment method based on the observability analysis for the strapdown inertial navigation system, which is able to meet the practical need of maintaining the navigation accuracy of the airborne pod. The observability of each state variable is obtained by observability analysis of system state variables. According to the weight of the observability, a transfer alignment filter algorithm based on adaptive adjustment factor is constructed to reduce the influence of weak observability state variables on the whole filter, which can improve the estimation accuracy of transfer alignment. Simulations and experiment tests of the airborne pod and the master strapdown inertial navigation systems show that the adaptive transfer alignment method based on the observability analysis can overcome the shortage of the weak observability state variables, so as to improve the alignment and the navigation performance in practical applications, thus improving the adaptability of the airborne pod.

Enhanced Disturbance Suppression Method Based on NonlinearH∞Filtering for Distributed POS in Aerial Earth Observation Imaging Application

Multinode remote sensing imaging loads have long been a fascinating focus of aerial earth observation applications, where the load denotes the device or equipment located on the aircraft for a specific task, such as aerial camera, scanning laser, and synthetic aperture radar. In order to guarantee high-quality imaging, distributed position and orientation system (POS) serves as high accuracy spatio-temporal reference information for remote sensing loads to realize motion compensation. As a result of the working environment, the flexible structure of the wing will suffer the external disturbance, then for distributed POS, the lever arm between the master system below the abdomen and slave system under the wing is time-varying, also the noise disturbance influences markedly on the motion parameters measurement accuracy. Then aiming at the transfer alignment accuracy improvement of distributed POS, a central difference <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filtering algorithm is proposed, which adopts the central difference Kalman filtering to realize nonlinear state estimation and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> filtering to suppress the disturbance influence on estimation, then the high accuracy motion parameters can be obtained. The flight test results show that the developed algorithm can effectively deal with the disturbance influence on transfer alignment estimation and enhance the parameter estimation accuracy, which can be used in aerial earth observation imaging.

A Motion Information Acquisition Algorithm of Multiantenna SAR Installed on Flexible and Discontinuous Structure Based on Distributed POS

Airborne multi-antenna SAR urgently needs the high-precision motion information of each antenna to realize high-precision imaging. Distributed Position and Orientation System (POS) is an important way to solve this problem based on transfer alignment. Because multiple antennas are usually installed on the flexible and discontinuous structure, there are complex and time-varying relative motions between different antennas. This relative motion will lead to the decline of transfer alignment accuracy, and directly affects the imaging accuracy of SAR. To solve this problem, a motion information acquisition algorithm of multi-antenna SAR installed on flexible and discontinuous structure based on distributed POS is given. Firstly, the gyroscopes and accelerometers of the distributed POS are used to calculate the relative attitude between nodes, which is applicable to both continuous and discontinuous structure. Secondly, combining the smooth estimation and the distributed filter, a distributed smoothing algorithm including forward filter and backward smoothing is proposed to calculate the high-precision and smoothed motion data of each sub-node. The forward filter obtains the motion parameters of each sub-node through transfer alignment based on the above relative attitude data. The backward smoothing further improves the motion information accuracy of sub-node by using the measurement data at all time and sharing the data of all nodes. Through the flight imaging test of multi-antenna SAR installed on a flexible and discontinuous platform, it is shown that the proposed algorithm can significantly improve the accuracy of the sub-node motion data, in which the errors of position, velocity and attitude are reduced by 58.9%, 61.9% and 71.2% respectively. Based on this motion data, SAR achieves better imaging focusing effect.

An innovative distributed filter for airborne distributed position and orientation system

Airborne earth observation with multiple imaging sensors requires high-precision motion information of multiple nodes on aircraft wings. Distributed position and orientation system (DPOS) is an important way to meet this acquirement, in which the high-precision motion parameters of sub-nodes are calculated by transfer alignment. Due to the complex elastic deformation between the nodes and the different accuracy of deformation data, the transfer alignment accuracy of each sub-node is inconsistent. Focus on this problem, a novel deformation measurement method based on accelerometer and an innovative distributed filter are proposed. Firstly, the wing deformation is calculated by relationship of the acceleration between master node and sub-node. The deformation data includes bending angle and deflection. Then a novel distributed filter is conducted, which includes three parts. The first part is to determine the relation model of motion information between different nodes. This relation model is calculated based on the above deformation data. The second part is sensor-level inertial measurement fusion, which can use the accelerometer data with small bias to amend gyroscope data with serious drift and improve sensor data precision of all nodes. The third part is system-level navigation data fusion, which fuses the navigation data of all nodes and improve the motion information precision of all nodes. The dynamic vibration experiments show that the proposed deformation measurement method can achieve better measurement accuracy than fiber brag grating sensor, in which the deflection error is reduced by 20%, and the standard deviation of bending angle error is reduced by 55%. Based on the above deformation data, the innovative distributed filter can further reduce the position error obviously. Especially, the height error can be decreased by over 36%.

A Time Synchronization Method Between FBG Deformation Measurement System and Airborne DPOS Based on Waveform Matching

The airborne Distributed Position and Orientation System (DPOS) is an important means for multi-point motion measurement required for earth observation with multi-task sensing loads. The sub-nodes in DPOS rely on the main node to perform transfer alignment for them to get high-precision motion data, which requires accurate deformation data and the relative motion relationship between different nodes. Fiber Bragg Grating (FBG) is one of the main means of deformation measurement at present. However, the time synchronization error between FBG and DPOS directly restricts the accuracy of transfer alignment. For this, a high-precision time synchronization method based on waveform matching is presented. Firstly, the data correlation between FBG and DPOS is analyzed. Then the main time error factors, sampling frequency and time delay are determined. On this basis, the empirical mode decomposition and wavelet analysis are used to extract and match the vibration data from the FBGS and bending angle data calculated by DPOS, thus updating the time-varying sampling frequency and time delay of FBGS, and finally achieving high-precision time synchronization between FBGS and DPOS. Results of ground dynamic experiment show that the standard deviation and mean value of FBG time error is less than 4ms and 2ms respectively, and the deformation displacement error of synchronized FBG data is less than 1%, which provides a prerequisite for high-precision transfer alignment of DPOS.

Six-Dimensional Deformation Measurement of Distributed POS Based on FBG Sensors

Distributed Position and Orientation System (POS) is a key equipment which can precisely measure multi-node time-spatial motion information for the high-resolution imaging of novel airborne remote sensing systems. The deformation of the lever-arm with irregular cross-section will seriously affect the transfer alignment accuracy of distributed POS. The existing Ko method can measure the deformation of most lever-arms, but it is not applicable to the lever-arm with irregular cross-section and cannot measure the axial displacement which is an important parameter of distributed POS. In order to solve this problem, a six-dimensional deformation measurement method of distributed POS based on Fiber Bragg grating (FBG) sensors is proposed. Firstly, a strain decoupling approach is presented to improve the strain measurement accuracy of the lever-arm with irregular cross-section. Secondly, an axial displacement model is established to further realize six-dimensional deformation measurement. The high precision six-dimensional deformation can be used for transfer alignment to improve the motion information measurement accuracy of distributed POS. Experimental results show the proposed method can measure the six-dimensional deformation with a higher accuracy compared to the existing Ko method.

Applied singular value decomposition method in transfer alignment and bias calibration

The transfer alignment accuracy between the master inertial navigation system (MINS), the slave inertial navigation system (SINS) and the online calibration capability of SINS are the important factors that affect the system performance. The traditional Kalman filtering-based transfer alignment method greatly depends on the prior information. The alignment and calibration based on optimisation-based alignment are preliminarily achieved without the prior information, but many deductions are required to achieve an optimal solution. In this study, a novel method aimed at transfer alignment and bias calibration is proposed, which is based on singular value decomposition by the inertial measurement vector matching equation to construct the least-squares solution. The performance of the system is analysed from the transfer alignment accuracy and the inertial instrument error estimation, respectively. The simulation and flight experiment results demonstrate that the proposed method is concise and effective without complex deduction and calculation. The relative attitude angle between MINS and SINS can be estimated accurately as well as the bias of SINS.

Adaptive UT-H∞ Filter for SINS’ Transfer Alignment Under Uncertain Disturbances

Accurate and rapid transfer alignment with large attitude errors under uncertain disturbances is crucial for the strapdown inertial navigation system (SINS). This paper proposes an adaptive UT-Hoc filter which combines UKF technology and a Hoc filter to increase the robustness of the nonlinear transfer alignment system. By focusing on the time-varying and the uncertain external disturbances, the robustness factor of the adaptive UT-H∞ filter can be adaptively adjusted to balance the robustness and filtering accuracy of the dynamic system. Then, the nonlinear error propagation model of the transfer alignment is established in detail, and the velocity plus attitude matrix measurement model is used to improve the performance of transfer alignment. Moreover, the sensor error compensation model is established to calibrate and compensate for the sensor errors of the gyros and accelerometers online during transfer alignment. The vehicle transfer alignment experiments show that the proposed adaptive UT-Hoc filter can significantly improve the transfer alignment accuracy and the pure inertial navigation accuracy compared with the existing filtering methods under uncertain disturbances.

Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy.

Airborne Distributed POS Flexible Baseline Measurement Method Based on MCLS

The distributed position and orientation measurement system (DPOS) depends on transfer alignment to accurately measure multi-node motion information for multi-task imaging sensors. However, the transfer alignment accuracy is limited by the flexible baseline accuracy between nodes. A flexible baseline measurement method based on the multiple camera laser-rangefinder measurement system (MCLS) is proposed in this paper. The relative position and orientation information between the nodes are measured directly by using MCLS. Combined with the advantages of visual measurement and laser ranging, this method completes the non-contact measurement and overcomes the difficult problem of the poor precision of the flexible baseline caused by inaccurate mathematical modeling. In order to verify this method, a distributed flexible baseline simulation platform is built and static test experiments are carried out. It shows that this method can realize sub-millimeter level measurement. At the same time, it can provide a strong support for the application of DPOS.

An Improved Adaptive Compensation H∞ Filtering Method for the SINS' Transfer Alignment Under a Complex Dynamic Environment.

Transfer alignment on a moving base under a complex dynamic environment is one of the toughest challenges in a strapdown inertial navigation system (SINS). With the aim of improving rapidity and accuracy, velocity plus attitude matching is applied in the transfer alignment model. Meanwhile, the error compensation model is established to calibrate and compensate the errors of inertial sensors online. To suppress the filtering divergence during the process of transfer alignment, this paper proposes an improved adaptive compensation H∞ filtering method. The cause of filtering divergence has been analyzed carefully and the corresponding adjustment and optimization have been made in the proposed adaptive compensation H∞ filter. In order to balance accuracy and robustness of the transfer alignment system, the robustness factor of the adaptive compensation H∞ filter can be dynamically adjusted according to the complex external environment. The aerial transfer alignment experiments illustrate that the adaptive compensation H∞ filter can effectively improve the transfer alignment accuracy and the pure inertial navigation accuracy under a complex dynamic environment, which verifies the advantage of the proposed method.

Multi-Node Transfer Alignment Based on Mechanics Modeling for Airborne DPOS

This paper deals with the problem of multi-node transfer alignment estimation of airborne distributed position and orientation system (DPOS), and its aim is to achieve the motion parameters for all sub-nodes as high precision as possible by using the main system. The complicated aircraft deformation, especially the wing’s flexure, will seriously decrease the accuracy of transfer alignment. Usually, the deformation between the main node and each sub-node is idealized as an independent Markov process. In fact, these deformations are interrelated at a certain moment. To solve the mentioned problem, a multi-node transfer alignment method based on mechanics modeling is proposed in this paper. In this method, the elastic mechanics theory is used to build a unified kinematic equation of the wing to describe the flexible deformations of all sub-nodes, and then the transfer alignment is performed to obtain the motion parameters of each sub-node with inertial measurement units (IMU). Finally, the motion parameters of other sub-nodes without IMUs are obtained by the least squares fitting. The mathematical simulation and semi-physical simulation based on flight experiment show that the proposed method is not only effective but also provides us some new insights into the multi-node measurement of DPOS.

A transfer alignment method for airborne distributed POS with three-dimensional aircraft flexure angles

An airborne distributed position and orientation system (POS) appears to satisfy the requirement of multi-point motion parameters measurement. This relies on transfer alignment from a high precision master system to slave systems to obtain high accuracy motion parameters of all points. A key problem for a distributed POS involves determining a method to treat the aircraft flexure appropriately and achieve high precision transfer alignment. In this study, the effect of aircraft flexure on transfer alignment accuracy for airborne earth observation is first analyzed. Based on this, the error model of transfer alignment that considers three-dimensional flexure angles are established, and a transfer alignment based on parameter identification unscented Rauch-Tung-Striebel smoother (PIURTSS) is proposed. The simulations results show that the transfer alignment method based on PIURTSS effectively improves the estimation accuracy.

A Unified Nonsingular Rapid Transfer Alignment Solution for Tactical Weapon Based on Matrix Kalman Filter

The performance of tactical weapons inertial navigation system (INS) greatly depends on the accuracy and rapidity of transfer alignment (TA). The major challenge is the estimation of the attitude of INS rapidly and accurately, under the condition of random initial misalignment angles and launch time constraint in tactical scenarios. The objective of this paper is to derive a unified nonsingular alignment scheme for rapid TA at random initial misalignment angles, which transforms the alignment problem into the estimation of the relative attitude matrix between the master INS and slave INS. Employing the attitude matrix as estimate state, we formulate a unified nonsingular linear dynamics model allowing us to estimate the attitude matrix directly. Furthermore, the attitude matrix estimation algorithm has been extended to work in a state matrix Kalman filter (MKF) framework, which we call MKFTA algorithm. It works without any assumption on the misalignment angles. Finally, Monte Carlo simulations demonstrate that the MKFTA can converge within 10 s and exhibits a better transient and steady-state accuracy than the traditional scheme.

A Novel Adaptive H∞ Filtering Method with Delay Compensation for the Transfer Alignment of Strapdown Inertial Navigation Systems.

Transfer alignment is always a key technology in a strapdown inertial navigation system (SINS) because of its rapidity and accuracy. In this paper a transfer alignment model is established, which contains the SINS error model and the measurement model. The time delay in the process of transfer alignment is analyzed, and an H∞ filtering method with delay compensation is presented. Then the H∞ filtering theory and the robust mechanism of H∞ filter are deduced and analyzed in detail. In order to improve the transfer alignment accuracy in SINS with time delay, an adaptive H∞ filtering method with delay compensation is proposed. Since the robustness factor plays an important role in the filtering process and has effect on the filtering accuracy, the adaptive H∞ filter with delay compensation can adjust the value of robustness factor adaptively according to the dynamic external environment. The vehicle transfer alignment experiment indicates that by using the adaptive H∞ filtering method with delay compensation, the transfer alignment accuracy and the pure inertial navigation accuracy can be dramatically improved, which demonstrates the superiority of the proposed filtering method.

Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS.

The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H filter (SIHF) for improving both the accuracy and robustness of RTA. In this new nonlinear H filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H filter for the first time, and the resulting SIHF simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIHF and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H filter. Moreover, the SIHF inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.