Redundant Inertial Measurement Unit Research Articles

Integrity monitoring for decentralized redundant IMUs/GNSS integrated navigation system with correlated measurements

In civil aviation systems, the inertial measurement unit (IMU)/Global Navigation Satellite System (GNSS) integrated navigation remains the most effective navigation scheme. Based upon this, the utilization of redundant IMUs and a multi-constellation system can significantly enhance navigation and integrity performance. However, redundant IMUs/GNSS integrated navigation systems are susceptible to more complex failure modes, including GNSS faults (satellite and constellation faults) and IMU faults. Additionally, dealing with correlated measurements between redundant constructions presents a challenging issue. Therefore, we propose an integrity monitoring method for decentralized redundant IMUs/GNSS integrated navigation systems. This method considers GNSS faults and IMU fault in the integrity monitoring process, specifically designed for decentralized filter construction with redundancy. Furthermore, due to the correlated measurements between redundant filters, optimal fusion coefficients are determined by considering the unbiased estimate and minimizing the trace covariance of the primary filter. An optimal allocation scheme for continuity risk is also established to improve navigation and integrity performance. Simulation results demonstrate the feasibility and effectiveness of the proposed integrity monitoring method. Moreover, in the context of correlated measurements, the integrated navigation system also exhibits superiority.

IBRNet: Interpretable Belief Rule Network modeling method for fault diagnosis of redundant inertial navigation systems

With the increasing complexity of inertial navigation systems, especially with the incorporation of redundant inertial measurement units, fault diagnosis is facing new challenges. On one hand, it involves the effective integration of system mechanisms and angular increment data. On the other hand, it focuses on the interpretability of the fault diagnosis models and their corresponding diagnostic results. To address these issues, this article proposes a new belief rule network structure. It takes into account interpretability constraints during the optimization process. The proposed structure, called IBRNet, aims to achieve fault diagnosis of redundant measurement units. Specifically, the belief rule network is used to model semi-quantitative information, and interpretability is achieved through two components: parameter optimization based on interpretability constraints and interpretability analysis based on Kernel SHAP. Additionally, a fault diagnosis model based on IBRNet was established for the inertial measurement unit with 5-table redundancy. Experimental results show that the proposed IBRNet method can provide higher accuracy and interpretability compared to several existing methods.

Using Functionally Redundant Inertial Measurement Units to Increase Reliability and Ensure Fault Tolerance

This paper aims to assess the possibility of using functionally redundant inertial units to solve problems of increasing reliability and ensuring the fault tolerance of the various classes and purposes of aircraft navigation systems. We present the results of studying failure detection methods to improve the accuracy and reliability of a strapdown functionally redundant inertial unit. The resulting structural redundancy of the strapdown inertial measurement unit is designed to increase the fault tolerance and accuracy of strapdown inertial navigation systems. The methods for detecting sensor failures in functionally redundant inertial units are based on the use of the equations of functionally redundant inertial unit compliance to nominal requirements for the accuracy of measuring the input action vector. To describe the methods for detecting and eliminating failed sensor and algorithm designs based on them, we gave the mathematical models of the measurement vector of functionally redundant inertial units concerning the measured vector and the error identification condition, including the residual of the matching equations with the size due to the level of redundancy, determining the total number of matching equations. The main criterion for determining a failed sensor is non-compliance with the nominal value of the residual included in a certain number of matching equations of the information received from such meters. The developed algorithms are examined using simulation methods. The study of the selected structure of the functionally redundant inertial units shows that the proposed approaches are efficient. Also, we manage to identify the main characteristics of the algorithms for detecting sensor failures that are structurally a part of the functionally redundant inertial units.

A ROBUST AUTONOMOUS VEHICULAR NAVIGATION SYSTEM USING RIMU-BASED INS/GNSS INTEGRATED SCHEME

Abstract. The application of the INS/GNSS integrated system is widespread in vehicle navigation. However, in certain complex urban areas, achieving accurate positioning for autonomous vehicles at level 3 and beyond presents a challenge. Moreover, ensuring the continuous availability of the navigation system is crucial for maintaining vehicle safety under all circumstances. Consequently, the specifications of the Inertial Measurement Unit (IMU), which have a direct correlation with cost, dominate the performance of the navigation system in environments where GNSS signals are denied or challenging. To implement level 3 autonomous vehicles, a strategy based on redundant IMUs (RIMU) is employed to not only enhance accuracy and availability but reduce the overall cost of the navigation system. This research mainly focuses on the development of a robust autonomous vehicular navigation system by designing an integrated algorithm for the low-cost RIMU and GNSS receiver within the INS/GNSS integration scheme. The positioning performance is analyzed by using a reference system and a benchmark system in the open sky, GNSS challenging, and GNSS outage scenarios.

Online Calibration of RIMU Based on Multistage EKF

Redundant inertial measurement units (RIMUs) are more reliable than normal IMUs, but they are suffering from the calibration of non-orthogonal gyroscopes and accelerometers. In this paper, aiming at the problem of relying on the orthogonal axis IMU information and error term coupling in the online calibration of RIMUs, a method based on the multi-stage EKF algorism is proposed. In this method, the calibration model of the inertial system is established, which does not depend on the orthogonality with the inclined axis inertial system, meanwhile, the attitude, velocity, and position information is taken as the measurement, and a filtering model is established based on the EKF method. Based on this model, the observability of the calibration method is proved, and the window and threshold for secondary filtering are set to reduce the coupling effect of scale factor error and installation error. The digital simulation indicates that the proposed method can provide high-precision online calibration results for the inclined axis scale factor error and installation error of RIMUs, and the calibration error is below 10%.

Self-Calibration Method for the Errors of Dual-Axis Turntable in Redundant Rotational Inertial Navigation System

Attitude measurement is an important capability of a redundant rotational inertial navigation system (RRINS). The attitude of RRINS is converted from the attitude of the redundant inertial measurement unit (RIMU) using a turntable. The attitude measurement accuracy of RRINS is reduced greatly by turntable errors because the turntable is rotated frequently when the RRINS is working. Therefore, it is important to calibrate the turntable errors for improving the attitude measurement accuracy of RRINS. In this study, a self-calibration method for the errors of a dual-axis turntable in RRINS is proposed. First, an error model of the dual-axis turntable is established, and the influence of turntable errors on RRINS attitude measurement is analyzed. Second, a self-calibration method, including rotation scheme, numerical algorithm (NA) errors calculation, and fruit fly optimization algorithm (FOA) errors estimation, is proposed. The influence of Earth’s rotational rate and gyroscope biases is eliminated during calibration using the designed rotation scheme, NA calculates the errors directly when they are minor angles, and FOA estimates the errors whether they are minor or large angles. The simulations and experiment results show that the proposed method can improve the attitude measurement accuracy of RRINS.

A Dual-Axis Rotation Scheme for Redundant Rotational Inertial Navigation System

A redundant rotational inertial navigation system (RRINS) comprises a redundant inertial measurement unit (RIMU) and a turntable for improving reliability and navigation accuracy. Because of the multi-sensor configuration, the RIMU has a more complex error model compared with the traditional orthogonal inertial measurement unit (IMU). Therefore, the RIMU-based rotation scheme cannot simply replicate the traditional IMU-based rotation scheme. In this study, a dual-axis rotation scheme for RIMU characteristics is proposed. First, the error model of the RIMU was established, and the error compensation of RIMU caused by rotation was analyzed. Second, the principles of rotation axis switching and reciprocating rotation were summarized, and a dual-axis rotation scheme was designed by these principles. Finally, the rotation scheme was applied to an RRINS prototype consisting of RIMU (four fiber optic gyroscopes + four quartz accelerometers) and a dual-axis turntable, and then simulations and experiments were performed. The results of the simulations and experiments show that the positioning accuracy of RRINS can be obviously improved by using the proposed rotation scheme.

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.

Enhancing navigation integrity for Urban Air Mobility with redundant inertial sensors

Urban Air Mobility (UAM) uses highly automated air vehicles to offer safe and efficient low-altitude urban air transportation services, for which ensuring navigation safety (measured by integrity) is an essential precondition. UAM vehicles can navigate themselves using Global Navigation Satellite Systems (GNSS)/Inertial Measurement Unit (IMU) tightly coupled systems. However, these systems are vulnerable to IMU failures aside from GNSS faults, given that UAM vehicles will use low-cost IMUs. IMU faults can severely degrade navigation integrity, thereby threatening UAM operational safety and increasing the difficulty of urban air traffic management. This issue can be mitigated by employing redundant IMUs. Civil aircraft use consistency checks among multiple high-performance Inertial Navigation Systems (INSs) to enhance navigation integrity. But this approach is not suitable for low-cost IMUs because of fast error accumulation. In response, this work proposes a new approach to enhance navigation integrity for UAM by integrating multiple IMUs with GNSS. In this approach, multiple IMUs and GNSS are tightly coupled by a centralized Kalman filter, and the corresponding integrity monitoring algorithm is developed to (a) detect the faults in GNSS and/or IMUs and (b) evaluate the probabilistic upper bounds on navigation errors, namely protection levels. Simulations are conducted to demonstrate the proposed method and to present sensitivity analyses, and the results suggest the effectiveness of the new approach. This method is beneficial to improving UAM operational safety and can potentially be applied to civil aircraft in the future.

Optimal Configuration Based on Lever Arm Effect Rejection Performance Index for Redundant Inertial Sensors

Redundant inertial sensor systems improve navigation performance and guarantee fault detection or fault isolation functions. However, they may cause unexpected side effects owing to their enlarged size. The misalignment between the center of gravity of the sensor system and the rotational origin of the vehicle results in the lever arm effect inducing significant acceleration errors. To minimize the lever arm effect error, the configuration of redundant sensors should be carefully considered, as their performance is highly dependent on its configuration. Thus, the figure-of-merit (FOM), which is considered a performance index or cost function, is proposed to obtain the optimal configuration for minimizing the lever arm effect error. Further, to define an FOM the angular velocity and angular acceleration are considered as random variables and to quantify them, they were replaced as variances. The lever arm effect error was found to be completely eliminated when a redundant inertial measurement unit (RIMU) with more than eight single-axis inertial sensors was configured using the proposed FOM. Further, a case study and simulation were conducted to verify the proposed FOM and configuration, and it was found that the proposed FOM can be used to obtain the optimal configuration of the RIMU and as an indicator of the lever arm effect error.

Data Fusion in Redundant Inertial Measurement Unit Using a Fruit-Fly-Optimized Weighted Least Squares Algorithm

Data Fusion in Redundant Inertial Measurement Unit Using a Fruit-Fly-Optimized Weighted Least Squares Algorithm

A novel MEMS-RIMU self-calibration method based on gravity vector observation

The microelectromechanical system (MEMS) redundant inertial measurement unit (RIMU) is a low-cost navigation system, which can improve reliability effectively. In existing MEMS-RIMU calibration schemes, extra equipment with higher accuracy is required. However, on some occasions, a rotation platform or other reference equipment cannot be provided, and calibration of the MEMS-RIMU is limited. To solve the problem, a novel self-calibration method for the MEMS-RIMU based on gravity vector observation is proposed, which can estimate the bias, scale errors and alignment errors of accelerometers and gyroscopes without extra equipment or rotation platforms. In this paper, a novel calibration model for accelerometers and gyroscopes is built by analyzing the relationship between gravity vector observation and the measurement value of the MEMS-RIMU. There is no attitude information in the calibration model, so the RIMU can be calibrated without attitude references via extra equipment. Then, a derivative unscented Kalman filter is proposed to estimate the bias, scale errors and alignment errors of accelerometers and gyroscopes, which can reduce the computation load. Finally, the simulation and experimental results demonstrate the efficiency and accuracy of the proposed calibration scheme.

Novel Methods of Mitigating Lever Arm Effect in Redundant IMU

In this paper, two novel methods to mitigate lever arm effect in Redundant Inertial Measurement Units (RIMUs) are proposed with different approaches. With the presence of a lever arm for each sensing axis, the unexpected accelerations such as Euler and centrifugal accelerations are added to the measurements through rotational motion, resulting in an estimation error of linear acceleration. Therefore, it was previously considered as the best option to minimize the length of the lever arm and compensate the lever arm effect from the accelerometer measurements. However, this approach cannot completely remove the estimation error as the compensated value is based on the estimated angular rates, where the magnitude of the error becomes more apparent with a RIMU composed of low-grade gyroscopes that shows a higher noise level. In order to solve this problem, we propose two methods that can mitigate estimation error using the specific arrangement of the lever arm vectors and the concentrated likelihood method-based nonlinear least squares (NLS). By the proposed methods, the accuracy in compensating the lever arm effect can be increased by placing the lever arm vectors symmetrically or using the information from accelerometers when estimating angular rates. Besides, the suggested methods each have their own advantages in computational efficiency and overall navigation performance, compared to previous method. The effectiveness of the proposed methods is verified through simulations including misalignments of each redundant sensor.

Reduction of time errors of the inertial measuring module by realization of its structural redundancy on the basis of three-axis micromechanical measuring instruments

Redundant inertial measurement units (IMU) are used in security-critical operations since the advent of inertial technology. This approach allows you to create fault-tolerant systems that can detect and isolate defective sensors. Besides, experimental results have shown that redundant IMU is also an effective way to improve the performance of navigation systems. The question is only in the dependence of the accuracy of the unit on the number of sensors used and their mutual orientation.
 This paper analyses the influence of spatial orientation on the accuracy of an IMU with a redundant configuration in the case of using triaxial orthogonal micromechanical measuring modules as atomic structural units.
 The first part of this article concentrates on the geometry of the redundant IMU. Analysis of its metrological model of a redundant showed that, when dealing with orthogonal IMU triads, its resulting accuracy is independent of the relative orientation between them. This fact presents important practical implications since it demonstrates that using complex large-scale installation structures can be avoided. As a result, it is enough to place instead of an array of units, for example, only on one printed circuit board with any orientation. Also, it was found the relationship between the number of sensors employed and the accuracy improvement that enables us to ascertain the exact number of sensors needed to design a navigation system with a certain precision.
 The second part of this article shows the experimental approval of theoretical conclusions during the testing of a prototype block based on three low-cost units (MPU6050) built according to a symmetric tetrahedron scheme.
 The accuracy of the redundant block was experimentally evaluated based on the value of the errors in determining the modulus of gravity acceleration and a given angular velocity of a test rotary platform in a series of positions. The performance of the tested inertial measuring block was better on average in comparison with anyone module from this block that proved the possibility of using these approaches for MEMS sensors in high-accuracy application areas.

On-Orbit Calibration Method for Redundant IMU Based on Satellite Navigation & Star Sensor Information Fusion

Aiming at the long term and high-precision performance maintenance requirements of the satellite-borne four-axis redundant inertial measurement unit (IMU) in space, the satellite navigation velocity information, the attitude information provided by the star sensor and the redundant measurement output are used as references to derive the measurement model, including the star sensor installation error. Three rotation sequences of optimal observation, sub-optimal observation, and under-observation are designed and compared. The Kalman filter is used to calibrate the redundant IMU in the orbit. The numerical simulation shows that all three rotation sequences can calibrate more than 91% of the constant error, which meets the requirements of the use, and the calibration accuracy of the optimal rotation sequence is the highest. The ground repeatability calibration experiment is carried out 10 times for the optimal rotation sequence. The calibration results show that the range of all the error terms is less than its threshold, illustrating that the calibration result is stable and reliable. It is a real-time calibration method for all error terms on orbit, providing a reference method for the high-precision performance maintenance of the spacecraft integrated navigation system.

A Novel Algorithm for the Fault Diagnosis of a Redundant Inertial Measurement Unit

Although a number of fault diagnosis algorithms for inertial sensors have been proposed in previous decades, the performance of these algorithms needs to be improved with regard to small faults. In this paper, we introduce a data driven-based algorithm, namely, SaPD, for the anomaly detection and output reconstruction of a redundant inertial measurement unit (RIMU). SaPD implements the fault identification of an inertial apparatus by combining an artificial neural network with the Q contribution plots method in parity space. To improve the performance of the fault detection part, in particular for small faults, we introduce a novel hyperplane that measures the distances between inputs and the primary-neuron set obtained from a self-organizing incremental neural network (SOINN). We also employ the Q contribution plots of sensors in the fault isolation part by analyzing historical data with principal component analysis (PCA). We perform quantitative evaluations in a realistic simulation environment, which demonstrates that the proposed SaPD algorithm outperforms other related algorithms in terms of the fault identification accuracy of tiny faults with an acceptable computational complexity.

An Anomaly Diagnosis Method for Redundant Inertial Measurement Unit and Its Application with Micro-Electro-Mechanical System Sensors

The application of the micro-electro-mechanical system inertial measurement unit has become a new research hotspot in the field of inertial navigation. In order to solve the problems of the poor accuracy and stability of micro-electro-mechanical system sensors, redundant design is an effective method under the restriction of current technology. Redundant data processing is the most important part in the micro-electro-mechanical system redundant inertial navigation system, which includes the processing of anomaly data and the fusion estimation of redundant data. To further improve the reliability of the micro-electro-mechanical system redundant inertial measurement unit, an anomaly detection, isolation, and recognition method for data anomalies is proposed. The relationship between the parity space method detection function and the deterioration degree of anomaly data is mathematically deduced. The parity space method detection functions of different anomalies are analyzed, and five indicators are designed to quantitatively analyze the detection function values. According to these indicators, the detection and recognition method are proposed. The new method is tested by a series of simulation experiments.

High-Precision Calibration Scheme for RIMU

Due to the diversity of redundant configuration schemes, the existing calibration algorithm for the redundant inertial measurement unit (RIMU) cannot be applied generally. In addition, the linearized calibration model utilized in the traditional calibration algorithms also leads to low calibration accuracy. In this paper, a high-precision calibration scheme is proposed for the RIMU, which can be applied generally. At first, a general calibration model for the RIMU is established. Then, a zero-space amplification algorithm is applied to improve the observability of the system. At last, the extended Kalman filter (EKF) based on the second-order Taylor series expansion is proposed, which can estimate the biases, scale factor errors, and installation errors simultaneously. The simulation and experimental results demonstrate that the proposed calibration algorithm calibrates the system accurately and improves the performance of the RIMU effectively. Furthermore, the proposed algorithm can be applied generally in all kinds of RIMU.

Data Fusion Architectures for Orthogonal Redundant Inertial Measurement Units.

This work looks at the exploitation of large numbers of orthogonal redundant inertial measurement units. Specifically, the paper analyses centralized and distributed architectures in the context of data fusion algorithms for those sensors. For both architectures, data fusion algorithms based on Kalman filter are developed. Some of those algorithms consider sensors location, whereas the others do not, but all estimate the sensors bias. A fault detection algorithm, based on residual analysis, is also proposed. Monte-Carlo simulations show better performance for the centralized architecture with an algorithm considering sensors location. Due to a better estimation of the sensors bias, the latter provides the most precise and accurate estimates and the best fault detection. However, it requires a much longer computational time. An analysis of the sensors bias correlation is also done. Based on the simulations, the biases correlation has a small effect on the attitude rate estimation, but a very significant one on the acceleration estimation.

Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization

Fault detection and isolation with redundant strapdown inertial measurement unit is critical for ensuring the reliability of the guidance or navigation system in the fields of both aeronautics and astronautics. Although the parity space approach is used widely, it cannot detect the soft fault which affects navigation performance under pulse quantization. This paper develops the three-channel filters to detect the soft fault and conducts theoretical implementation. The constraint conditions of their parameters are explored and the influence of the weight of different ratios is analyzed. The Monte Carlo simulation is carried out in order to verify the validity of the fault detection and isolation method. The simulation results and their analysis provide a theoretical reference for fault detection and isolation with redundant strapdown inertial measurement unit.