Strapdown Inertial Navigation System Error Research Articles

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

Virtual Metrology Filter-Based Algorithms for Estimating Constant Ocean Current Velocity

The strap-down inertial navigation system (SINS) and Doppler velocity log (DVL) integrated navigation system are widely used for autonomous underwater vehicles (AUVs). Whereas DVL works in the water tracking mode, the velocity provided by DVL is relative to the current layer and cannot be directly used to suppress the divergence of SINS errors. Therefore, the estimation and compensation of the ocean current velocity play an essential role in improving navigation positioning accuracy. In recent works, ocean currents are considered constant over a short term in small areas. In the common KF algorithm with the ocean current as a state vector, the current velocity cannot be estimated because the current velocity and the SINS velocity error are coupled. In this paper, two virtual metrology filter (VMF) methods are proposed for estimating the velocity of ocean currents based on the properties that the currents remain unchanged at the adjacent moments. New measurement equations are constructed to decouple the current velocity and the SINS velocity error, respectively. Simulations and lake tests show that both proposed methods are effective in estimating the current velocity, and each has its advantages in estimating the ocean current velocity or the misalignment angle.

Estimation of constant ocean current velocity based on SINS/DVL integrated navigation with an augmented observable quantities filter

Ideally, navigation and positioning information for Autonomous Underwater Vehicles (AUVs) can be provided by a combined navigation system based on a velocity-matching Strap-down Inertial Navigation System (SINS) and Doppler velocity log (DVL). However, the velocity accuracy of DVL is affected by ocean currents when the DVL is operating in water track mode in deep ocean scenarios. The velocity of the currents remains stable over small areas of the ocean within a few hours and can be considered a constant value. Estimation and compensation of the ocean current velocity is a prerequisite for high-precision navigation performance and an important topic for oceanographic research. In this paper, we propose an Augmented Observable Quantities (AOQ)-based filter that constructs a new measurement equation from the difference between the velocities of the previous state and the current state to decouple the ocean current velocity from the velocity error of SINS, and without introducing other sources to provide any information on velocity or position. The simulation and lake test show that the AOQ filter can estimate the ocean current velocity and mitigate its effect on navigation accuracy.

A novel adaptive nonlinear Kalman filter scheme for DVL-aided SINS alignment in underwater vehicles

The rapid and accurate initial alignment of a strapdown inertial navigation system (SINS) aided by a Doppler velocity logger (DVL) still poses a significant challenge in unmanned underwater vehicle navigation. In this study, a novel adaptive nonlinear filtering alignment scheme is developed for a SINS with a large misalignment angle. In previous studies, the frame-inconsistency problem of the nonlinear SINS error model was neglected. Thus, a modified nonlinear error model of a SINS is derived here rigorously as the process model of the nonlinear Kalman filter. In addition, the noise amplification of applying Sage-Husa adaptive Kalman filter (SHAKF) in alignment is analyzed, and a novel arctangent fading memorial factor is introduced to dynamically adjust the weight of current measurements. Simulations and field experiments indicate that the proposed algorithm is efficient in underwater DVL/SINS in-motion alignment. The alignment accuracy and convergence rate of the modified model are significantly improved compared to those of the traditional model. The arctangent fading memorial SHAKF can reliably estimate the measurement noise and improve the performance of alignment.

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Due to low cost and complementary performance advantages, global navigation satellite system (GNSS)/ strapdown inertial navigation system (SINS) integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm, and the non-zero lateral velocity generated by the turning maneuver are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 min. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

Virtual global positioning system construction approach for unmanned surface vessel based on Dempster–Shafer theory and broad learning framework

AbstractIntegrated navigation systems made up of a strap-down inertial navigation system (SINS) and global positioning system (GPS) are increasingly being used to improve the position, speed, and attitude information of unmanned surface vessels (USV). However, a GPS outage could occur due to the dependence of GPS performance on the external environment and the number of available satellites. This study uses an innovative combination of Dempster–Shafer (DS) theory and broad learning (BL) method to design a SINS/GPS integrated navigation system. First, the velocity and position information derived from the SINS and their corresponding GPS were fused using DS fusion rules, while the SINS error was modelled using the BL method. A ‘virtual’ GPS was then designed using the proposed DS–BL approach to provide the speed and position information when the GPS signal was interrupted, thereby ensuring the continuous navigation of the USV. The results of both simulation and sea trial demonstrate that the proposed virtual GPS estimation approach is effective, and the navigational accuracy of the proposed method is superior to other methods.

Experimental strapdown inertial navigation system for the autonomous guidance system of a short-range uav

The task of experimental verification of the possibility of using a miniature strapdown inertial navigation system (SINS) on micromechanical sensors for autonomous guidance of a short-range manned aircraft moving along a programmed hypothetical trajectory is considered.
 A mathematical model of the first approximation of the instrumental errors of the two-coordinate autonomous SINS of the aircraft with a short flight time was obtained. The mathematical model allows both to calculate the values of these errors for specific types of inertial sensors used in SINS (direct analysis problem), and to choose the types of accelerometers and gyroscopes based on ensuring the given accuracy of SINS (inverse synthesis problem). The requirements for permissible errors of accelerometers and gyroscopes of the SINS sensor unit have been determined.
 The design and manufacturing technology of an experimental SINS based on a triaxial micromechanical accelerometer and three uniaxial micromechanical gyroscopes are described.
 The technology of flight tests of the experimental SINS is considered. The test results of the experimental SINS of the aircraft, created in accordance with the requirements specified in the article, confirmed the correctness of the chosen approach to ensuring the accuracy of the SINS for the autonomous inertial guidance system of short-range aircraft.

A Compensation Algorithm with Motion Constraint in DVL/SINS Tightly Coupled Positioning

Integration of strapdown inertial navigation system (SINS) and doppler velocity log (DVL) is usually applied in underwater applications, wherein DVL provides the three-dimensional velocity, and hence the accumulated error of SINS can be compensated. However, the DVL/SINS loosely coupled approach fails in the complex environments on the condition of fewer than three available beams. And the tightly coupled approach divergences with only one direction velocity information from the beam measurement. To solve the problems, a novel tightly coupled method is proposed in this paper, in which the state of UUV motion is considered, and a robust adaptive Kalman filter is utilized to dynamically estimate the observation noise. Experiment results indicate that the proposed method is efficient in UUV missions for beam limited environment.

Self-aided SINS for spiral-diving human-occupied vehicle in midwater

PurposeMotivated by the problems that the positioning error of strap-down inertial navigation system (SINS) accumulates over time and few sensors are available for midwater navigation, this paper aims to propose a self-aided SINS scheme for the spiral-diving human-occupied vehicle (HOV) based on the characteristics of maneuvering pattern and SINS error propagation.Design/methodology/approachFirst, the navigation equations of SINS are simultaneously executed twice with the same inertial measurement unit (IMU) data as input to obtain two sets of SINS. Then, to deal with the horizontal velocity provided by one SINS, a delay-correction high-pass filter without phase shift and amplitude attenuation is designed. Finally, the horizontal velocity after processing is used to integrate with other SINS.FindingsSimulation results indicate that the horizontal positioning error of the proposed scheme is less than 0.1 m when an HOV executes spiral diving to 7,000 meters under the sea and it is inherently able to estimate significant sensors biases.Originality/valueThe proposed scheme can provide a precise navigation solution without error growth for spiral-diving HOV on the condition that only IMU is required as a navigation sensor.

Virtual DVL Reconstruction Method for an Integrated Navigation System Based on DS-LSSVM Algorithm

Autonomous underwater vehicle (AUV) mostly relies on an integrated navigation system, which consists of a strap-down inertial navigation system (SINS) and Doppler velocity log (DVL). The integrated system provides continuous and accurate navigation information when compared to stand-alone SINS or DVL. However, the dependence of DVL signals on the acoustic environment may cause any DVL malfunction due to marine organisms or strong wave-absorbing material. This article introduces a novel method utilizing Dempster–Shafer (DS) theory augmented by least squares support vector machines (LSSVMs) known as DS-LSSVM. The SINS and DVL data fusion are designed by DS theory whereas LSSVM models the SINS error. The virtual DVL is built by the proposed DS-LSSVM method, which makes AUV navigation purposes possible during the long-term DVL outage. The test results demonstrate the effectiveness of the proposed virtual DVL signal estimation method. In addition, the positioning accuracy of the proposed method outperforms that of the LSSVM method.

SINS/DVL Integrated System With Current and Misalignment Estimation for Midwater Navigation

Motivated by the problem that water-track Doppler Velocity Log (DVL) cannot effectively suppress the error accumulation of strap-down inertial navigation system (SINS), this paper proposes a novel SINS/DVL integrated navigation algorithm for deep and long cruising range Human Occupied Vehicle (HOV). Such algorithm decomposes the navigation process into two tightly coupled working modes: alignment mode and navigation mode. In the alignment mode, HOV is controlled to perform horizontal circular motion for several minutes to realize Self-Aided SINS. Combining the precise navigation solutions provided by Self-Aided SINS and measurements from DVL with water track, recursive least square (RLS) algorithm is adopted to estimate heading misalignment angle between SINS and DVL and horizontal ocean current velocity. In the navigation mode, both horizontal ocean current velocity obtained in the alignment mode and DVL measurements are utilized to assist SINS, thus enabling SINS/DVL/Current integrated navigation. A square trajectory with a navigation-grade inertial measurement unit (IMU) is simulated to evaluate the proposed SINS/DVL integrated navigation algorithm. Simulation results show that the proposed SINS/DVL integrated navigation is capable of suppressing SINS error divergence effectively and efficiently. In addition, the feasibility and effectiveness of Self-Aided SINS based on horizontal circular motion is also verified by field test with real IMU data.

Improving the Horizontal Components Accuracy of Strapdown Airborne Vector Gravimetry by Yaw Continuous Rotation Modulation

The accuracy of gravity disturbance horizontal components is an obstacle in strapdown airborne vector gravimetry at present. In the strapdown inertial navigation system (SINS) field, the rotation modulation technique has been put into practice to improve the accuracy of navigation for decades, which could suppress the divergence of SINS errors. Since there is a close connection between SINS and strapdown vector gravimetry, we were inspired to research on introducing rotation modulation into strapdown airborne vector gravimetry in this study. In this article, the second section introduced the connections and differences between SINS and vector gravimetry. Then, the third section analyzed three main obstacles in improving the horizontal components accuracy of strapdown airborne vector gravimetry. To solve these problems, the fourth section proposed the mechanism of rotation modulation vector gravimetry from system level which is inspired by rotation modulation inertial navigation system (RMINS). On the basis of the mechanism, the fifth section designed a yaw continuous rotation modulation scheme for vector gravimetry. Simulation and real-world static experiment demonstrated that the designed scheme has a better performance in horizontal components than the classical strapdown gravimetry. Moreover, the experiment with drift showed that rotation modulation could also suppress the effects of the accelerometer long-term drift on gravimetry results. In conclusion, this article introduced the rotation modulation into strapdown airborne vector gravimetry for the first time. It will probably provide a reference for developing the next-generation strapdown airborne vector gravimeter.

Analysis of Deflection of Vertical Compensation for Inertial Navigation System

With the development of high-precision inertial navigation systems, the deflection of vertical (DOV), gravity disturbance, is still one of the main error sources that restrict navigation accuracy. For the DOV compensation of the Strapdown Inertial Navigation System (SINS) problem, the influences of the calculation degree of the spherical harmonic coefficient and the calculation error of the DOV on the compensation effect were studied. Based on the SINS error model, the error propagation characteristics of the DOV in SINS were analyzed. In addition, the high-precision global gravity field spherical harmonic model EIGEN-6C4 was established and the influence comparative analysis of the calculation degree of the spherical harmonic coefficient on the DOV compensation of SINS in different regions was carried out. Besides, the influence of the calculation error of the DOV on the compensation was emphatically analyzed. Finally, the vehicle experiment verified the feasibility of compensation in SINS based on the gravity field spherical harmonic model. The simulation and experiment results show that it is necessary to consider the influence of the calculation degree and the calculation error of the DOV on the compensation for long-time high-precision SINS with the position accuracy of 0.3 nm/h, while the SINS with general requirements for position accuracy can ignore the impact.

Accurate Integrated Navigation Method Based on Medium Precision Strapdown Inertial Navigation System

A method of accurate integrated navigation for high-altitude aerocraft by medium precision strapdown inertial navigation system (SINS), star sensor, and global navigation satellite system (GNSS) is researched in this paper. The system error sources of SINS and star sensor are analyzed and modeled, and then system errors of SINS and star sensor are chosen as system states of integrated navigation. Considering that the output of star sensor is attitude quaternion, it can be regarded as an attitude matrix, then the equivalent attitude matrix is constructed by using the output of SINS, and the calculating equation of the equivalent attitude matrix is designed. Thus, one of the measurements of integrated navigation can be constructed by using the equivalent attitude matrix and the attitude matrix output of star sensor. According to the constraint conditions of the attitude matrix, the diagonal elements are selected as one of the measurements of integrated navigation, and the corresponding measurement equation is derived. At the same time, the velocity output and position output difference between SINS and GNSS is selected as the other measurement, and the corresponding measurement equation is also derived. On this basis, the Kalman filter is used to design an integrated navigation filtering algorithm. Simulation results show that although the medium precision SINS is used, the heading accuracy of this integrated navigation method is better than ±1.5′, the pitch and roll accuracy are better than ±0.9’, the velocity accuracy is better than ±0.05 m/s, and the position accuracy is better than ±3.8 m. Therefore, the integrated navigation effect is very significant.

Non-initial attitude error analysis of SINS by error closed-loop and energy-level constructing

For the optimization and algorithm design of high precision strapdown inertial navigation system (SINS), a systematic and fine attitude error analysis method is needed. To this end, a closed-loop method and an energy-level method are proposed to analyze the non-initial attitude estimation error of SINS. The closed-loop method is constructed from the basic angular velocity error equation in which angular velocity error is combined with angular velocity and rotation estimate to form a closed loop. This makes it the first time to realize the complete (theoretically unbiased) analysis of SINS rotation estimation error. The energy-level method is constructed from the energy level of angular motion which is expressed as the series expansion of rotation estimate under the defined motion such as coning and maneuvering motion. This enables us to finely analyze the error of any existing multi-sample coning algorithm based on Taylor-series method. The theoretical basis, establishment process and instructions of two methods are given in order in the paper. The detailed process of proof and reasoning of the two methods are given in Appendixes A, B, C and D. Several cases of attitude error analysis based on the proposed methods and simulation examples verify the correctness of the analysis results from the proposed method and the applicability of the proposed method, which provides a systematic analysis tool for the design of SINS and attitude algorithm.

A Low-Cost Method of Improving the GNSS/SINS Integrated Navigation System Using Multiple Receivers

GNSS (global navigation satellite system) and SINS (strap-down inertial navigation system) integrated navigation systems have been the apparatus for providing reliable and stable position and velocity information (PV). Commonly, there are two solutions to improve the GNSS/SINS integration navigation system accuracy, i.e., employing GNSS with higher position accuracy in the integration system or utilizing the high-grade inertial measurement unit (IMU) to construct the integration system. However, technologies such as RTK (real-time kinematic) and PPP (precise point positioning) that improve GNSS positioning accuracy have higher costs and they cannot work under high dynamic environments. Also, an IMU with high accuracy will lead to a higher cost and larger volume, therefore, a low-cost method to enhance the GNSS/SINS integration accuracy is of great significance. In this paper, multiple receivers based on the GNSS/SINS integrated navigation system are proposed with the aim of providing more precise PV information. Since the chip-scale receivers are cheap, the deployment of multiple receivers in the GNSS/SINS integration will not significantly increase the cost. In addition, two different filtering methods with central and cascaded structure are employed to process the multiple receivers and SINS integration. In the centralized integration filter method, measurements from multiple receivers are directly processed to estimate the SINS errors state vectors. However, the computation load increases heavily due to the rising dimension of the measurement vector. Therefore, a cascaded integration filter structure is also employed to distribute the processing of the multiple receiver and SINS integration. In the cascaded processing method, each receiver is regarded as an individual “sensor”, and a standard federated Kalman filter (FKF) is implemented to obtain an optimal estimation of the navigation solutions. In this paper, a simulation and a field tests are carried out to assess the influence of the number of receivers on the PV accuracy. A detailed analysis of these position and velocity results is presented and the improvements in the PV accuracy demonstrate the effectiveness of the proposed method.

An quantitative method for observability analysis and its application in SINS calibration

In the strapdown inertial navigation system (SINS) calibration, the observability of SINS error states has significant impacts on calibration results. In order to analyze the observability of SINS error states quantitatively, this paper proposes the observability degree for the states of linear time-varying systems. It is shown that the proposed observability degree illustrates the sensitivity of state estimations to disturbances and determines the upper bound of estimation accuracy. Based on the proposed observability degree, the sufficient and necessary conditions for the states to be observable are presented. In addition, an experiment of ship-mounted SINS calibration is given to validate the proposed observability degree. According to the experiment results, the proposed observability degree can make accurate evaluations on the observability of SINS error states, thus providing direct understandings on the accuracy of calibration results.

Fault-Tolerant GNSS/SINS/DVL/CNS Integrated Navigation and Positioning Mechanism Based on Adaptive Information Sharing Factors

Real-time positioning feature is becoming an essential part in a variety of military and civilian applications. In particular, the cooperation of multisensors helps to improve navigation accuracy and the universality of the system. In this article, we propose a novel robust fault-tolerant federated filter based on strapdown inertial navigation system (SINS), global navigation satellite system (GNSS), celestial navigation system (CNS), and doppler velocity log (DVL). In the proposed algorithm, the position information of the GNSS, the velocity information of the DVL, and the attitude information of the CNS are introduced as measurement information to correct the divergence error of SINS. The federated filter is exploited for its flexibility and capability in fault toleration. To amend fault and recover information in local filters, a simplified state Chi-square test (SSCST) is utilized as the fault detection, isolation, and recovery procedure. Meanwhile, a new adaptive information sharing factor algorithm based on the results of SSCST and the residuals between the actual observations and the predicted observations is designed, which can adaptively reflect the performance of each local filter. Experimental results show that the accuracy of the proposed integrated navigation and positioning algorithm can be improved and the system remains stable even when the error occurs.

A Fault-Tolerant Polar Grid SINS/DVL/USBL Integrated Navigation Algorithm Based on the Centralized Filter and Relative Position Measurement.

Navigation is a precondition for ocean space vehicles to work safely in polar regions. The traditional polar algorithms employ the grid strapdown inertial navigation system (SINS) as the backbone and Doppler velocity log (DVL) output velocity as measurements to constitute the integrated navigation system, of which, however, the position errors still accumulate with time. The ultra-short baseline (USBL) position system can provide position information that can be used to improve the performance of the SINS/DVL integrated system. Therefore, a grid SINS/DVL/USBL integrated algorithm for polar navigation is proposed in this paper. In order to extend the availability of the USBL and improve integration accuracy in polar regions, the USBL observation model is established based on the relative position measurement firstly. Then, a grid SINS/DVL/USBL integrated algorithm is proposed to fuse the information of these sensors with a modified Kalman filter (MKF) dealing with the sparse USBL output. Finally, a vector fault detection method, which takes the measurements as detection objects instead of the filter, is designed to locate the measurement fault and can be employed by the centralized filter to improve the fault-tolerant. Simulation and experiment results show that the proposed grid SINS/DVL/USBL integrated navigation system can further restrain SINS errors especially the position errors effectively. Meanwhile, the vector fault detection method can detect and isolate the fault measurements of centralized filter immediately and accurately. Therefore, the proposed fault-tolerant grid SINS/DVL/USBL integrated navigation algorithm can improve the reliability and accuracy of polar navigation for ocean space application.

Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft.

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.