Strapdown Inertial Navigation System Research Articles

State transformation extended Kalman filter–based tightly coupled strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter integration for seamless navigation of unmanned ground vehicle in urban areas

With the rapid development of unmanned ground vehicle industry, how to achieve continuous, reliable, and high-accuracy navigation becomes very important. At present, the integrated navigation with global navigation satellite system and strapdown inertial navigation system is the most mature and effective navigation technology for unmanned ground vehicle. However, this technique depends on the signal accuracy of global navigation satellite system. When the receiver cannot capture four or more satellite signals for a long time or the satellite completely invalid, it cannot provide accurate navigation and positioning information for the unmanned ground vehicle. Therefore, this article combine the observation information of strapdown inertial navigation system, global navigation satellite system, and laser Doppler velocimeter to propose a high-precision seamless navigation technique of unmanned ground vehicle based on state transformation extended Kalman filter. Under different land vehicle driving environments and global navigation satellite system signal quality conditions, the seamless navigation technique is evaluated through global navigation satellite system interruption simulation and land vehicle experiments. The experimental results show that the strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter tightly coupled integration seamless navigation has good environmental adaptability and reliability and can maintain high navigation accuracy under high frequency global navigation satellite system–signal blockage conditions in urban areas.

Methodology of Airborne Gravimetry Surveying and Strapdown Gravimeter Data Processing

The paper presents a methodology of preparing and conducting airborne gravimetry survey using an inertial measuring unit (IMU) or strapdown inertial navigation system as airborne gravimeter. We also discuss the key aspects of developing appropriate postprocessing algorithms and software and their application to gravimeter raw data processing. Solving the strapdown inertial airborne gravimetry problem, we use readings of the IMU sensors (accelerometers and gyroscopes) as primary information. The second equally important source of information is raw (pseudorange, Doppler pseudorange rate, and carrier phase) measurements from the GNSS receivers on board the aircraft and on the ground. The developed methodology, algorithms, and practical recommendations presented in the paper are based on the authors’ long-term experience in airborne gravimetry.

A Fast Intelligent Algorithm-Based Positioning Error Suppression Method for Strap-Down Inertial Navigation System

It is a difficult problem to suppress navigation errors of strap-down inertial navigation system (SINS) when external observation is rejected or discontinuous. With the help of intelligent algorithms, the device errors of SINS can be searched and compensated when the vehicle has no significant linear motion, so as to limit the positioning error of inertial navigation system (INS) to a certain extent in the case of external observation rejection. However, the INS contains long period error terms. In order to suppress the navigation errors, long time data need to be observed and then trained by intelligent algorithms, which limits the usability of the correlation error suppression algorithms in practice. In view of this problem, this article proposes a fast intelligent algorithm (FIA)-based positioning error suppression method for INSs. By virtually extending the inertial navigation update interval, the divergence law of the longitude error could be changed. On this basis, a fast training algorithm based on particle swarm optimization (PSO) is designed. The contributions of the work presented here are twofold. First, the mathematical relationship between the positioning error and the device error of SINS with virtually extended update interval is analyzed. Second, compared with the traditional methods, the amount of data required for training is greatly reduced. Therefore, the device errors of INS could be searched and compensated with short-term observation data. Then, the long-term positioning accuracy of INS can be improved efficiently. Field tests are performed, which validate the efficacy of the proposed method.

A time asynchronous parameters calibration method of high-precision FOG-IMU based on a single-axis continuous rotation scheme

There is a time asynchronous problem with the output signals between each inertial sensor in an actual high-precision fiber optic gyro inertial measurement unit (FOG-IMU), which reduces the velocity update accuracy of the strap-down inertial navigation system in the actual applications. Firstly, the velocity error formulas are derived in the coordinate navigation system considering three time asynchronous errors between the three-axis accelerometers and gyros. Then an error-state Kalman filter is designed with respect to the time asynchronous parameters measurement model in angular dynamic environments. Afterward, a single-axis continuous rotation scheme is developed to calibrate the time asynchronous parameters. In particular, a simple observability analysis model is proposed to verify the rationality of the calibration scheme. Furthermore, the time asynchronous parameters are calculated effectively based on the proposed indirect calibration scheme. Finally, the proposed method is verified effectively through simulations and turntable experiments, which can significantly reduce the measurement errors of high-precision FOG-IMU. As a result, compared with the traditional three-parameter direct calibration method, the eastward and northward velocity errors are reduced by 7.01% and 2.48%, respectively.

Performance Evaluation of Stereo Vision Aided Loosely Coupled GNSS/SINS Integration for Land Vehicle Navigation in Different Urban Environments

In urban environments, the positioning performance of global navigation satellite system (GNSS) degrades severely due to the harsh environment, which will be improved after fused with a strap-down inertial navigation system (SINS). However, due to the error accumulation, the positioning ability of GNSS/SINS integration will also decrease significantly during frequent GNSS outages. To improve the positioning accuracy, we use stereo vision to enhance the performance of GNSS/SINS integration, where GNSS and stereo vision are loosely coupled and tightly coupled with SINS under the Earth-centered Earth-fixed (ECEF) frame, respectively. To evaluate the positioning performance of the multisensor fusion system, two in-field experiments were conducted to assess the contribution of stereo vision. The results show that with the support of stereo vision and SINS, GNSS accuracy can be considerably improved by 60%–80% in complex environments, which also outperforms the GNSS/SINS integration. Besides, detailed analyses in various urban environments further demonstrate that stereo vision can significantly improve the performance of GNSS/SINS integration when a long-term GNSS anomaly occurs, but it shows no improvement for a short-term GNSS anomaly.

МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ И ПРАКТИЧЕСКИЕ АСПЕКТЫ РЕШЕНИЯ ЗАДАЧ ОПТИМИЗАЦИИ НЕСУЩИХ СИСТЕМ БЕСПЛАТФОРМЕННЫХ ИНЕРЦИАЛЬНЫХ НАВИГАЦИОННЫХ СИСТЕМ

This article describes approaches to solving problems of optimization of bearing structureof strapdown inertial navigation systems (SINS). A typical optimization problem in this case ismultiobjective parametric optimization of the bearing structure of the SINS accelerometer triad inorder to minimize the mass of the bearing structure and minimize deviation angles of the accelerometeraxes under the action of external loads. The ANSYS Mechanical and ANSYSDesignXplorer modules are used as a tool for numerical modeling and optimization, respectively.Practical issues related to parameterization of SINS bearing structure 3D-models, calculation ofaccelerometer axes deviation angles, possible variants of numerical experiment plans, estimationof response sensitivity to input parameters, generation and refinement of the response surface, andmultiobjective optimization are considered. For the rational parametrization of geometry, theSINS device assembly was decomposed, as a result of which the parts and structural elements that have the greatest influence on the considered objective functions were identified. To calculate thedeviation angles of the sensitive elements axes, special two-node finite elements and relations forthe Bryant angles were used, which describe the relative position in space of two coordinate systems.When planning a numerical experiment, at the first stage of optimization, a central compositionplan was used, and at subsequent stages, the parameter space was filled using the Latin hypercubemethod with the option of relations between parameters, which made it possible to avoiddegenerate design options. The response surface was built using the genetic aggregation methodand subsequently refined based on a set of optimal solutions. Optimization for conflicting goals ofmass minimization and stiffness maximization was carried out using a multiobjective genetic algorithm.The described set of approaches to solving optimization problems as a result of an exemplaryseries of calculations made it possible to reduce the mass of a serial SINS bearing structurepart by 23% with fixed stiffness.

Correction of a Strapdown Inertial Navigation System During Descent in the Atmosphere

Correction of a Strapdown Inertial Navigation System During Descent in the Atmosphere

An In-Flight Alignment Method for Global Positioning System-Assisted Low Cost Strapdown Inertial Navigation System in Flight Body with Short-Endurance and High-Speed Rotation

Alignment technology plays an important role in navigation, and is used extensively throughout military and civilian applications. However, the existing in-flight alignment methods cannot be applied to the low-cost based strap-down inertial navigation system/global positioning system integrated navigation system, used in short-endurance and high-speed rotation flight bodies, since they cannot quickly obtain alignment results to meet the accuracy requirements of a flight body with special movement characteristics of short-endurance and high-speed rotation. In this paper, in order to solve this challenging problem of alignment for flight body with short-endurance and high-speed rotation, a fast in-flight alignment method based on the Lie group is proposed. First, an in-flight alignment model based on vector observations was established by using the Lie group. Second, addressing the problem that the alignment accuracy is greatly affected by the low-cost inertial sensor bias, an improved unscented Kalman filter was constructed in the Lie group on the basis of fully considering the characteristics of the system equations to estimate and feedback the correlated errors. Finally, a trajectory simulation of high-speed flight body and field semi-physical test was carried out to evaluate the proposed method. Evaluation of the system performance in comparison with existing state-of-the-art methods indicated that the proposed in-flight alignment method has better alignment accuracy and faster alignment velocity for a low-cost strap-down inertial navigation system/global positioning system integrated navigation system.

An improved comprehensive calibration method for ship strapdown inertial navigation system

To restrain the divergence of positioning errors in inertial navigation systems (INSs) and improve long-term navigation and positioning accuracies, comprehensive calibration technology is an important method. This paper improves on the basis of the previous method of determining the misalignment angles and equivalent gyroscope drift by the parameters identification method (PIM), and introduces the normal time-frequency transform (NTFT) method. The main work of this paper are as follows: (1) the relationship between the Schuler period oscillation (SPO) in the INS velocity and the misalignment angles are analyzed, and then the misalignment angles are calculated with the extracted velocity using NTFT; (2) PIM and NTFT are combined for comprehensive calibration, the attitude angles, velocities and latitude are reset, and the longitude is compensated with the equivalent gyroscope drift. Simulation and shipboard dynamic tests show that the NTFT/PIM can effectively restrain the amplitude of SPO error and improve the long-term navigation and positioning accuracies. From the perspective of practical application, the result of this study is a single-point calibration technology that improves the autonomy of the system.

SINS/DVL Linear Initial Alignment based on Lie Group SE3 (3)

This paper investigates the strapdown inertial navigation system (SINS) initial alignment aided by Doppler velocity log (DVL). A linear Kalman filtering method is proposed using the theory of Lie group, which is immune to the initial misalignment. The dead-reckoning position based on the body velocity provided by DVL and attitude provided by SINS is used as measurement. The SINS calculated attitude, velocity, position and DVL dead-reckoning position are formulated together as the elements of Lie group SE <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> (3). Since the dead-reckoning position is a type of left-invariant measurement, the corresponding linear error-state model is derived based on the left-invariant group error definition. Although the Lie group state model is not group affine, thesmall attitude error assumption does not introduce accuracy degeneracy in deriving the linear error-state model. This is the core reason why the derived linear error-state model is still applicable for cases with large initial misalignment. Experiments are conducted to verify the feasibility and effectiveness of the proposed methods. The test results show that the proposed method has higher accuracy and faster convergence speed as compared with the existing methods.

Virtual Beam Aided SINS/DVL Tightly Coupled Integration Method With Partial DVL Measurements

Strapdown inertial navigation system (SINS)/ Doppler velocity log (DVL) tightly coupled (TC) fusion is a popular scheme as it offers increased robustness compared to other approaches. In TC, DVL beam velocities are separately integrated with SINS in the navigation filter. To enhance the performance of SINS/DVL TC integration system in situations of partial DVL beam measurements, virtual beam aided solutions are proposed, theoretically derived, and experimentally evaluated. To that end, we analyze the geometrical relation between the DVL beam configuration and a zero velocity vector assumption and derive a zero velocity update (ZUPT) aided virtual beam method to continuously operate the TC fusion. Further, to avoid the precondition limitation of ZUPT method, a least squares support vector machine (LS-SVM) aided virtual beam construction algorithm together with its SINS/DVL TC integration scheme is proposed. Our experiments show the advantages and disadvantages of the ZUPT aided solution under different scenarios, and indicate the effectiveness of the LS-SVM aided virtual beam solution during short partial beam measurement periods.

High‐Precision Positioning Simulation and Experimental Research of Special Operation Vehicles Based on Network RTK

In terms of driverless systems, high‐precision positioning technology is one among the critical aspects of driverless cars to achieve driverlessness. This study analyzed the working principles of GNSS (global navigation satellite system) and SINS (strapdown inertial navigation system) and elaborated the principles of the least square method and LAMBDA algorithm in the integer ambiguity resolution. Based on the network RTK positioning technology and the abovementioned theory, the unmanned automatic work vehicle was used as the research object, and the fusion positioning algorithm of the BDS/GPS system and inertial sensor was used to propose a high‐precision positioning technology for the unmanned automatic work vehicle. The combined navigation system model was studied and constructed. Relevant verification was carried out through simulation and experiment. The results were as follows: the pitch angle error was less than 0.1°, the roll angle error was less than 0.05°, the speed error was less than 0.2 m/s, and the position error was less than 2.1 m. The outcomes indicate that an integrated navigation and positioning algorithm for driverless vehicles can significantly enhance the localisation accuracy and reliability of navigation. The research results are of engineering value and practical application for the development of unmanned automatic special vehicle positioning systems.

Strapdown Vehicle Gravimetry Nonmodel Error Compensation Method

In strapdown vehicle gravimetry, a new phenomenon was discovered by strapdown vehicle gravimetry error model analysis and simulation. That is, after compensating all error terms in the gravity measurement error model, there is still residual error in gravity results, which could not be described by the model. In response to this, multiscenario simulations were carried out to study the characteristics of this residual nonmodel error. Simulations manifest that the nonmodel error has close relationship with the navigation data recorded during strapdown vehicle gravity measurement. On the basis of this, this letter proposed a strapdown vehicle gravimetry nonmodel error compensation method. This method could estimate the nonmodel error by strapdown vehicle gravimetry simulation, employing the real integrated navigation data and the strapdown inertial navigation system (SINS) real trajectory generator. The effectiveness of the proposed method was verified by a real strapdown vehicle gravimetry test in Changsha. Test results show that the repeatability of the repeat lines under 200 s filtering has been improved from 0.51 to 0.41 mGal, 19.6% increase in accuracy. It demonstrates that the proposed method is correctness and validity in real strapdown vehicle gravimetry application.

Tightly Coupled Information Fusion for SINS/DVL/USBL Integrated Navigation of UUV

In order to improve the positioning accuracy and reliability of the navigation system in complex underwater environment, a strapdown inertial navigation system (SINS)/doppler velocity log (DVL)/Ultra-short baseline (USBL) tightly-coupled algorithm is proposed based on a centralized Kalman filter. A new 26-dimensional tightly-coupled measurement model is constructed. Different from the traditional loosely-coupled algorithm, the proposed model directly integrates the original measurement information of each sensor, instead of the navigation information such as position and velocity. Simulation and experiment results show that the proposed algorithm can make the best use of the only original information when the sensor cannot output the correct position and velocity in case of poor measurement conditions. Compared with the loosely-coupled algorithm, the proposed algorithm has better positioning accuracy and higher reliability.

A Super-High-Accuracy Attitude Measurement Method of SINS Based on PWQHN Algorithm in the High-Dynamic Maneuver Environment

To reduce noncommutative error (NCE) and improve the attitude-update accuracy in the attitude algorithm of the strapdown inertial navigation system (SINS), we assume that angular rate is represented as a polynomial to deduce the relationship between the angular increment sampling sequence and the derivatives of the quaternion. Subsequently, a high-order numerical solution of the quaternion differential equation is obtained by substituting the derivatives of the quaternion into its Taylor expansion. Generally, the quaternion will be constructed with a series of angular increment samples in a constant updating interval. Furthermore, the piecewise quaternion high-order numerical (PWQHN) algorithm is deduced by reconstructing quaternion in each sampling interval to ulteriorly improve attitude accuracy. The simulation results indicate that the PWQHN algorithm is stable in two kinds of simulation conditions. In the coning motion condition, PWQHN algorithm has the highest accuracy. In the high-dynamic condition, the extreme accuracy of PWQHN is not significantly higher than other algorithms. However, PWQHN can still output accurate attitude in the situation without many angular increment subsamples.

Invariant Error-Based Integrated Solution for SINS/DVL in Earth Frame: Extension and Comparison

The error propagation of traditional strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) is not autonomous because its error state model is trajectory-dependence. Recently, the invariant error defined on the Lie group has raised much attention due to its trajectory-independent and autonomous error propagation. In this article, the invariant error-based Kalman filter for the SINS/DVL integration solution is investigated with a main focus on its extension and comparison. The contributions of this study are threefold. First, the invariant error defined on the matrix Lie group (group of double direct isometrics) is extended to model the nongroup affine traditional SINS mechanism and the group affine transformed SINS mechanism; both of them are derived in Earth frame and augmented with the drift and bias of the inertial measurement units (IMUs). Then, the observation equations for different invariant error-based state models are derived for SINS/DVL application, a theoretical analysis is performed, and a comprehensive evaluation is conducted under different maneuvering conditions by lake field trial. Finally, the variational Bayesian approach is introduced into the invariant error-based Kalman filter to infer the inaccurate process noise covariance matrix (PNCM) and time-varying measurement noise covariance matrix (MNCM) from a practical perspective; experimental results demonstrate that it can improve the navigation accuracy significantly. This study is expected to facilitate the selection of appropriate invariant error to SINS/DVL applications.

Automated Tuning of Nonlinear Kalman Filters for Optimal Trajectory Tracking Performance of AUVs

The performance of navigation algorithms significantly determines the trajectory tracking accuracy of the guidance, navigation, and control (GNC) system of an autonomous underwater vehicle (AUV). In closed-loop operation, the interaction among path planning, control, and navigation plays a crucial role in the tracking accuracy of the overall GNC system. A Doppler velocity log (DVL) is often used for AUVs to measure velocity over the ground, positively affecting the closed-loop tracking error. However, a DVL may not be installed in miniaturized AUVs due to limited space and energy. In this paper, a navigation filter for an underactuated miniature AUV (nanoAUV) is considered that is mainly based on acoustic localization using a novel highly-miniaturized ultra-short baseline (USBL) system and a depth pressure sensor. The nanoAUV is being developed for subglacial lake exploration. We compare two unscented Kalman filters (UKF) with different prediction models - the classical strapdown inertial navigation systems (SINS) model and a hydrodynamic motion model (HMM). To enable a fair comparison, filter parameters are auto-tuned with Bayesian optimization (BO) for open-and closed-loop performance, which is novel in AUV navigation. The results indicate that BO performs similarly to particle swarm optimization (PSO) regarding sample efficiency for the proposed problem. To quantify the GNC tracking performance, we use extensive Monte Carlo simulations. Results suggest that with BO-tuned navigation filter parameters, the median tracking error is reduced by up to 50% compared to default parametrization.

In-Motion Alignment Method of SINS Under the Geographic Latitude Uncertainty

Initial alignment is vital to the strapdown inertial navigation system (SINS), and the in-motion initial alignment is a critical stage for underwater vehicles. In order to realize the in-motion alignment of the SINS under the geographic latitude uncertainty, we propose a latitude estimation method based on wavelet filtering. The external reference velocity is introduced, and the parameter adjustment strategy of the compass alignment control loop under different maneuvering modes is designed to improve the alignment accuracy. Firstly, the influence of line motion on the compass alignment is analyzed, and the in-motion compass alignment scheme of SINS based on the assistance of the external velocity is designed. Then, the error analysis is carried out by using the transfer function of the compass alignment loop. Moreover, the influence of different maneuverability on alignment accuracy is discussed. Finally, the setting strategy of control parameters of the compass alignment loop under different maneuvering modes is put forward. Simulation results are reported to validate the latitude estimation and in-motion alignment performance of the proposed method.

A Novel Tightly-Coupled SINS/RCNS Integrated Navigation Method Considering Atmospheric Density Error

With the increasing demands for high-accuracy autonomous navigation, the integrated strapdown inertial navigation system and refractive celestial navigation system (SINS/RCNS) has received close attention. Aided by stellar refraction information, it can correct position and velocity errors. However, atmospheric density error is a critical element that restricts the accuracy of SINS/RCNS. To solve this problem, this paper proposes a novel tightly-coupled method. In this scheme, the atmospheric density error is estimated and compensated to accommodate for the unknown model perturbation. Additionally, a tight integration model based on star pixel coordinates is derived to handle the time-varying measurement noise. The proposed method is evaluated by representative suborbital flight vehicle navigation simulations, and the results indicate that the introduction of atmospheric density error compensation can improve the navigation accuracy without adding extra sensors and enhance the performance.

A Novel Fault Detection and Isolation Technology for Redundant Inertial Measurement Unit

The fault detection and isolation (FDI) accuracy directly affects the redundant inertial measurement unit (RIMU) working state. The previous methods are difficult to apply to the four-axis system because they do not have enough information to allow fault isolation. Therefore, in this paper, an extended RIMU system is proposed to implement fault location in a four-axis system. The extended system consists of three additional accelerometers and a four-axis RIMU. The specific force information in the gyro-free strapdown inertial navigation system (GFSINS) is used to obtain reference angular velocity information. Then, based on the extended system, a fault detection procedure is designed to achieve faulty gyro isolation and system reconfiguration. Simulation results show that the extended system can effectively isolate gyroscope failures in a four-axis RIMU system. Compared to the non-redundant three-axis system, the extended system is 1.75 times more reliable and has 37% better average navigation accuracy.