Strapdown Inertial Navigation System Research Articles

Improving Strapdown Inertial Navigation System Performance by Self-Compensation of Inertial Sensor Errors

Abstract Microelectromechanical systems (MEMS)-based strapdown navigation systems offer advantages such as small size, low cost and minimal power consumption. However, MEMS sensors are prone to significant low-frequency noise and poor bias repeatability, which can lead to navigational errors over time. These errors make them unsuitable for autonomous navigation applications, even with frequent recalibration. One way in which to solve this problem is by using the rotation modulation (RM) method. This approach is widely recognised but has only been successful with precise laser and fiber optic gyroscopes equipped with precise rotating platforms. This article focuses on the potential of adapting the RM method for the case of inexpensive MEMS sensors that can significantly improve navigation performance, while maintaining the benefits of microelectromechanical technologies. Potential issues of implementation were discussed, and corresponding requirements were formulated. The proposed optimal computation scheme was verified during static tests of the developed inertial measurement unit (IMU). Further steps in studying the adaptation of the RM method for MEMS sensors have also been outlined.

Strapdown Inertial Navigation System Accuracy Improvement Methods Based on Inertial Measuring Unit Rotation: Analytical Review

Strapdown Inertial Navigation System Accuracy Improvement Methods Based on Inertial Measuring Unit Rotation: Analytical Review

Accelerated alighnment of inertial navigation system On movable base

It is proposed and researched algorithm of accelerated alignment in motion of an integrated navigation system, which consists of a strapdown inertial navigation system (SINS) and a satellite navigation system (SNS). Such a standard SNS is considered, in which orientation angles are not determined, that is, heading, pitch, roll. The algorithm is suitable for use on objects where the velocity vector coincides with the longitudinal axis of the object, that is, the heading angle coincides with the path angle. These can be ground vehicles, other vehicles at zero drift (wear) angles. The paper examines the estimation of the error of the heading alignment, because the heading alignment (gyrocompassing) is usually the most difficult procedure of the initial alignment. When determining the heading error, the benchmark is the heading that can be determined from the satellite speed readings or from the readings of the hybrid navigation system, which is built on the SINS algorithm, in which the speed is not determined, but the speed from the SNS is used. The study was conducted by modeling. At the same time, the standard characteristics of SNS, gyroscopes and accelerometers are taken into account. For comparison, a simulation of the well-known integrated SINS was also carried out using a weakly coupled open integration scheme (compensation scheme) using the Kalman filter (FK). It is shown that the determination of the error can be performed in a few seconds, if the SNS is already working in normal mode. The accuracy of the determination increases with an increase in the speed of movement and the frequency of issuing input data. The proposed algorithm shows higher accuracy than the FK algorithm. In the given example, for a ground object in 30 s, the accuracy of the alignment is less than 5 arc.min.

A Roadheader Positioning Method Based on Multi-Sensor Fusion

In coal mines, accurate positioning is vital for roadheader equipment. However, most roadheaders use a standalone strapdown inertial navigation system (SINS) which faces challenges like error accumulation, drift, initial alignment needs, temperature sensitivity, and the demand for high-quality sensors. In this paper, a roadheader Visual–Inertial Odometry (VIO) system is proposed, combining SINS and stereo visual odometry to adjust to coal mine environments. Given the inherently dimly lit conditions of coal mines, our system includes an image-enhancement module to preprocess images, aiding in feature matching for stereo visual odometry. Additionally, a Kalman filter merges the positional data from SINS and stereo visual odometry. When tested against three other methods on the KITTI and EuRoC datasets, our approach showed notable precision on the EBZ160M-2 Roadheader, with attitude errors less than 0.2751° and position discrepancies within 0.0328 m, proving its advantages over SINS.

Integrated navigation algorithm for deep-sea AUV based on M-estimation and heavy-tailed noise

This paper presents an integrated navigation algorithm for deep-sea Autonomous Underwater Vehicles (AUVs) based on M-estimation and heavy-tailed noise to solve the problems of inaccurate noise models in traditional navigation algorithms for deep-sea AUVs, significant delays in Ultra-Short Baseline (USBL) acoustic positioning, and the inability to estimate deep-sea ocean currents. The algorithm encompasses four techniques: ①A Strapdown Inertial Navigation Systems (SINS)/USBL integrated navigation algorithm based on M-estimation and heavy-tailed noise models. ②Estimation and compensation of USBL acoustic delay positioning errors. ③Estimation and compensation of steady or slowly varying ocean currents. ④Considering the distance between the submerged vehicle and the center of the earth and the variation of gravity acceleration with depth. The first and second techniques are the innovations of this article, which can effectively improve the USBL calibration accuracy with only a slight increase in computational complexity. The significant positioning error caused by long-term voyages of large-depth AUVs in the middle layer of the ocean can be effectively suppressed using the third and fourth technologies. The results of the semi-physical simulation and lake experiment demonstrate the ability of the proposed algorithm to significantly improve navigation accuracy and robustness in complex underwater environments compared to conventional methods.

Guiding an Unmanned Aerial Vehicle with a Thermal Imaging Correlation-Contrast Algorithm of Automatic Tracking in Conditions of Information Counteraction

The problem of constructing an algorithm for automatic tracking of a ground object for a guidance system (GS) for an unmanned aerial vehicle (UAV) is considered. The guidance system includes a thermal imaging target coordinator with a combined correlation-contrast direction finding algorithm and a strapdown inertial navigation system. Guidance takes place under conditions of information counteraction, which causes random interruptions in information and random changes in the power of interference, which are recorded by the corresponding indicators. A combined noise-resistant automatic tracking algorithm is obtained, using the readings of information interruption indicators, interference power, and measuring bearing angles, based on the theory of systems with a random jump structure. An example is given that illustrates the operation of the algorithm and demonstrates the satisfactory accuracy of automatic tracking.

Integration of SINS, laser doppler Velocimeter, and monocular visual odometry for autonomous navigation in complex road environments

For the integrated navigation method based on the strapdown inertial navigation system (SINS) and laser doppler velocimeter (LDV), the accuracy of the LDV is easily affected by the complex environment, such as sand and dust on the road, which leads to the degradation of the integrated navigation system’s positioning accuracy. In this paper, we propose an integrated navigation method for land vehicles that uses SINS, LDV, and monocular visual odometry (Mono-VO). We considered the boresight errors, lever-arm residual, and scale factor errors of Mono-VO when establishing an error model for the integrated SINS/LDV/Mono-VO navigation system. The new state vector’s observability is investigated. A Huber-based Kalman filter is also used to fuse these data. Finally, simulations and semi-physical simulations are carried out to validate the effectiveness of the proposed method, which demonstrates that positioning accuracy under complex road conditions is superior to that of the SINS/LDV navigation system.

A SINS/DVL navigation method based on hierarchical water velocity estimation*

Aiming at the problem that Doppler velocity log (DVL) cannot measure the velocity of the vehicle in the middle water environment, a Strap-down Inertial Navigation System (SINS)/DVL integrated navigation method based on hierarchical water velocity assistance is proposed. Firstly, according to the principle of DVL water layer flow measurement, the paper gives a hierarchical water recurrence principle. On this basis, a water velocity estimation method based on average weighted recurrence is proposed. The carrier depth information is introduced in this method, and the carrier velocity information is obtained by means of average value. This method can prevent the carrier velocity anomaly caused by the large error of single-layer water velocity measurement. Secondly, in view of the problem that the DVL water velocity measurement is easily disturbed in the middle water environment, a sliding window and a Chebyshev polynomial fitting function are introduced to realize the robust treatment of the water velocity. On this basis, combined with the Huber M robust filter algorithm, a hierarchical water velocity assisted integrated navigation algorithm based on the improved Huber M robust estimation is proposed to further improve the autonomous navigation accuracy in the middle water environment. Finally, the method proposed in this paper is verified by simulation. The results show that the hierarchical water velocity estimation method proposed in this paper can effectively estimate the water flow velocity, and its robust filtering algorithm can further improve the SINS/DVL integrated navigation accuracy in the middle water environment.

A multi-axis alternating continuous rotation scheme for tri-axis rotational inertial navigation system (RINS) with fiber optic gyro

Benefiting from the rotational modulation technique and the gimbal structure, rotational inertial navigation system (RINS) can improve navigation accuracy by about one order of magnitude compared with the strap-down inertial navigation system (SINS). By continuously rotating around a fixed axis, the constant and slow variability errors perpendicular to the direction of the rotation axis can be effectively restrained while the errors in the direction of the rotation axis cannot be suppressed. Therefore, a multi-axis rotation scheme needs to be designed. Meantime, the fiber optic gyro (FOG), as a very popular inertial device, is well suited for RINS but has a larger scale factor error and poorer stability than the laser gyro; thus, it is not suitable to flip the inertial measurement unit (IMU) around the horizontal axis too much in the rotation scheme to avoid the rapid accumulation of horizontal misalignment angle. Accordingly, we propose a multi-axis alternating continuous rotation scheme for tri-axis RINS with FOG. During the navigation process, the inner, middle and outer gimbals rotate forward and reverse in sequence. The IMU rotates continuously in the three-axis direction can effectively suppress the effects of constant and slow-varying errors and also avoid the large velocity errors oscillation caused by flipping the IMU. The simulations and navigation tests show that the multi-axis alternating continuous rotation scheme can achieve navigation accuracy comparable to the multi-position rotation scheme and reduce the oscillation of velocity errors, which has important research significance and engineering value for the research of RINS with FOG.

Fault-Tolerant SINS/Doppler Radar/Odometer Integrated Navigation Method Based on Two-Stage Fault Detection Structure.

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system.

SINS/GNSS-Integrated Navigation Based on Group Affine SINS Mechanization in Local-Level Frame

In this article, a group affine strapdown inertial navigation system (SINS) mechanization in local-level frame is investigated for SINS and global navigation satellite system (GNSS) integrated navigation. Traditionally, the SINS mechanization in local-level frame is strong coupling and, therefore, is not group affine. In order to make it group affine, two main procedures are presented. One is making use of damping calculation for SINS, that is, the Coriolis and centrifugal force are ingeniously calculated outside the SINS calculation and filtering recursion using known velocity and position information provided by GNSS. The other is introducing an auxiliary velocity. Then, the trajectory-independent left-invariant error state-space model is derived corresponding to the group affine model. The derived group affine model and its corresponding error state-space model can be used in the SINS/GNSS indirect integration. Simulations and field tests are conducted to evaluate the actual performance of the investigated method. The results show that the proposed method has a much superior performance for SINS/GNSS with large initial attitude error. In contrast, the proposed method has a similar performance with existing state-of-the-art-integrated navigation method when the initial attitude error is small.

Error suppression method of SINS/CNS integrated navigation based on diametrical stargazing

The strap-down inertial navigation system (SINS)/celestial navigation system (CNS) integrated navigation system has been extensively employed in the aerospace field owing to its unique advantages. The internal parameter errors of the star sensor constitute one of the primary factors that affect the navigation accuracy. In this paper, an error suppression method based on diametrical stargazing has been designed to suppress the navigation errors caused by the internal parameter errors of the star sensor. First, a SINS/CNS integrated navigation model based on Kalman filter has been established. Then the influence of the principal point error, image-plane tilt error, and distortions of the optical system on the measurement accuracy of the star sensor have been analyzed. Further, the process of introducing the errors into the measurement equation has been obtained. Finally, for multiple errors, two stargazing methods have been designed to suppress the impact of these errors on the navigation accuracy, whereas the constant errors of the inertial device can be modulated. The simulation results demonstrate that the proposed method can improve the positioning accuracy of the system, and verify the effectiveness of the algorithm.

A SINS/GNSS/2D-LDV integrated navigation scheme for unmanned ground vehicles

In recent years, unmanned autonomous driving technology has attracted increasing attention from people, and become a research hotspot. Currently, the integration of strapdown inertial navigation system (SINS) and global navigation satellite systems (GNSSs) is the most common and effective navigation and positioning scheme for unmanned ground vehicles (UGVs) and unmanned aerial vehicles. However, this integrated system is unable to maintain a reliable positioning solution in challenging environments due to the inherent weakness of GNSS signals and the accumulation of SINS positioning errors over time. To address this issue, this paper proposes an integrated scheme based on an asynchronous Kalman filter for SINS, GNSS and two-dimensional (2D) laser Doppler velocimeter (LDV). In the proposed scheme, the SINS and 2D-LDV are tightly coupled to improve the robustness of the integrated system, and the error parameters between the 2D-LDV and the SINS are calibrated in real time during the validity of the GNSS signal. In addition, the designed asynchronous Kalman filter method evaluates the validity of the GNSS and 2D-LDV measurements in real time based on Mahalanobis distance of innovation vector and statistical property principle. Two groups of long-distance, high-mobility vehicle experiments conducted in challenging environments verify the validity of the proposed scheme. The experimental results show that the proposed SINS/GNSS/2D-LDV integrated navigation scheme has good environmental adaptability and reliability, and can maintain high horizontal and vertical positioning accuracy despite frequent GNSS signal failures, which can meet the needs of UGVs.

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.

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.

Research on an Error Compensation Method of SINS of a Mine Monorail Crane

Underground coal mines belong to the GNSS-denied environment, and the Strapdown Inertial Navigation System (SINS) has a significant advantage in the precise positioning of equipment in this environment because of its operation without requiring interaction with external information and strong anti-interference capabilities. Nonetheless, the vibrations of the installation platform adversely affect the positioning accuracy of SINS. This article focuses on the monorail crane in coal mines as the subject of research, developing a dynamic model for the motion unit consisting of the “track + drive unit + driver’s cab”, while analyzing the relationship between track roughness conditions and the vibration excitation of this unit. Subsequently, utilizing the dynamic model, the study calculated the angular and linear vibration characteristics and formulated models to address coning error and sculling error specific to the SINS in this vibration condition. Lastly, by employing a multi-sample compensation algorithm, this article compensated for positioning errors in the SINS caused by track roughness-induced vibrations during uniform straight-line motion of the motion unit, thus achieving optimal positioning information for the monorail crane. The simulation results demonstrated that employing a four-sample compensation algorithm reduces the coning error in SINS positioning calculations by a minimum of 50% and decreases the sculling error by at least 31%, satisfying the positioning accuracy requirements for precise parking of the monorail crane during the transportation phase, while establishing the foundation for autonomous precise positioning and integrated navigation of underground track transport equipment in coal mines.

A novel parallel constrained extended Kalman filter for improving navigation algorithm – case study: gas pipeline

In many real navigation problems, moving objects may have some state or measurement constraints along their way. Using these constraints in conventional Extended Kalman Filter (EKF) equations results large matrices which are computationally time-consuming. In this paper, a new Constrained Navigation Filter (CNF) is proposed as a parallel to reduce the computational burden of the conventional EKF algorithm while increasing the positioning accuracy. So a methodology has been developed for Strap-down Inertial Navigation System (SINS) based on MEMS IMU applied on Pipeline Inspection Gauge (PIG) to sense data at constant sampling rate of 108 km of the pipeline. The results verified that using such a hybrid approach has improved positional accuracy 8.97% in comparison with that of the latest methods like EKF/ Pipe Line Junctions (PLJ). Also, the proposed method is 2.277 times better than EKF/PLJ in the algorithm runtime.

Novel Method for Coarse Alignment of Strapdown Ins On Oscillatory Base

Novel Analytic Coarse Alignment Method is presented for Strapdown Inertial Navigation Systems (SDINS) alignment on oscillatory base. Oscillatory base alignment is conducted by building two vectors derived from gravity and angular velocity information provided by the strapdown INS under oscillatory motion and comparing them with similar vectors built using the local level reference frame information. The theory and algorithms are detailed in the paper and several simulation test results are compiled for various kinds of oscillatory motion.The proposed algorithm could be used for sea based INS applications.

Strong Tracking UKF-Based Hybrid Algorithm and Its Application to Initial Alignment of Rotating SINS With Large Misalignment Angles

In this paper, the fast and precise initial alignment of the low-cost rotary strapdown inertial navigation system under mooring conditions is investigated. The unscented Kalman filter (UKF) is used to address model nonlinearity and to achieve fast alignment for large misalignment angles. A generalized multi-fading strong tracking UKF (GSTUKF) is proposed to effectively compensate for the kinematic model errors caused by mooring and rotary motions. The performance of the standard strong tracking UKF cannot be optimal because only a portion of the fading factors associated with the directly observable state variables can be obtained approximately using the analytical method. By contrast, using the iteration method to calculate the full-dimension fading factors precisely, the GSTUKF has greater robustness and adaptability. To find out the optimal fading factors in real time, the multivariate nonlinear optimization model is first designed, and then the iteration approach based on a hybrid algorithm consisting of particle swarm optimization and beetle antennae search is developed, which has no risk of falling into a local minimum. Simulations and experiments are conducted to validate the GSTUKF's effectiveness. The experiments demonstrate that the proposed method reduces the yaw error by 47.2% compared with the standard strong tracking UKF.

From Algorithm to Module: Adaptive and Energy-Efficient Quantization Method for Edge Artificial Intelligence in IoT Society

Next-generation industrial edge artificial intelligence (AI) applications will undoubtedly emerge on energy-efficient, highly-integrated platforms incorporating various sensors, processors, and functional modules. Effectively bridging sensor data captured from a microprocessor or central processing unit with dedicated processing modules is still being studied in academia and industry. Therefore, embedding the data quantization module onto edge AI chips, connecting sensors, processors, and functional modules, is critical to achieving adaptive transformation of diverse data representation formats. This paper proposes a novel adaptive and low-power quantization technique and systematically validates its effectiveness from algorithm to hardware module for industrial IoT applications, covering precise navigation for autonomous vehicles and accurate classification utilizing deep neural networks (DNNs). The proposed quantization method merges an adaptive conversion function from floating-point to fixed-point binaries and an adaptive radix-point determination function, ensuring adequate resolution and minimal error loss of the fixed-point inputs to the edge AI modules. The experimental results demonstrate that the quantization error in the proposed quantization technique contributes negligible errors to the navigation solutions of the strapdown inertial navigation system and the DNNs' top-1 and top-5 classification accuracy (on the order of 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-8}$</tex-math></inline-formula> and 10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{-7}$</tex-math></inline-formula> ). Moreover, a quantization-on-multiplier (QoM) hardware module is designed, synthesized, and routed in accordance with the proposed quantization technique. The simulation results indicate that the QoM's power consumption and area are 0.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$mW$</tex-math></inline-formula> and 649.552 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu m^{2}$</tex-math></inline-formula> , accounting for 5% and 14.15% of its total energy consumption and area, respectively. With our proposed on-chip quantization technique, the time required to quantize the parameters of DNNs is up to 1142 times shorter than with the existing benchmark off-chip quantization approaches.