scholarly journals Optimal Redundant Sensor Configuration for Accuracy and Reliability Increasing in Space Inertial Navigation Systems

2012 ◽  
Vol 66 (2) ◽  
pp. 199-208 ◽  
Author(s):  
Mahdi Jafari ◽  
Jafar Roshanian
Keyword(s):  
Inertial Measurement Unit ◽  
High Reliability ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Single Degree Of Freedom ◽  
Inertial Measurement ◽  
Geometric Configurations ◽  
General Derivation ◽  
Sensor Configuration

A redundant Inertial Measurement Unit (IMU) is an inertial sensing device composed of more than three accelerometers and three gyroscopes. This paper analyses the performance of redundant IMUs and their various sensor configurations. The inertial instruments can achieve high reliability for long periods of time only by redundancy. By suitable geometric configurations it is possible to extract the maximum amount of reliability and accuracy from a given number of redundant single-degree-of-freedom gyros or accelerometers. This paper gives a general derivation of the optimum matrix which can be applied to the outputs of any combination of three or more sensors to obtain three orthogonal vector components based on their geometric configuration and error characteristics. Certain combinations of four or more instruments are able to detect an instrument malfunction, and combinations of five have the additional capability of isolating that malfunction to a particular sensor. Finally, this paper offers a major improvement in reliability, although the improvement in accuracy is minor.

Global Observability Analysis IMU/Camera Integration

2013 ◽  
Vol 380-384 ◽  
pp. 1069-1072
Author(s):  
Qiang Fang ◽  
Xin Sheng Huang
Keyword(s):  
State Estimation ◽  
Inertial Measurement Unit ◽  
Sufficient Conditions ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Inertial Measurement ◽  
Observability Analysis ◽  
Inertial Measurements

Vision-aided inertial navigation systems can provide precise state estimates for the 3-D motion of a vehicle. This is achieved by combining inertial measurements from an inertial measurement unit (IMU) with visual observations from a camera. Observability is a key aspect of the state estimation problem of INS/Camera. In most previous research, conservative observability concepts based on Lie derivatives have extensively been used to characterize the estimability properties. In this paper, we present a novel approache to investigate the observability of INS/Camera: global observability. The global observability method directly starts from the basic observability definition. The global observability analysis approach is not only straightforward and comprehensive but also provides us with new insights compared with conventional methods. Some sufficient conditions for the global observability of the system is provided.

scholarly journals Scalar Method of Fault Diagnosis of Inertial Measurement Unit

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Vadym Avrutov
Keyword(s):  
Fault Diagnosis ◽  
Inertial Measurement Unit ◽  
Calibration Method ◽  
Quality Monitoring ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Working Capacity ◽  
Element Determination ◽  
Inertial Measurement ◽  
Algorithm Verification

The scalar method of fault diagnosis systems of the inertial measurement unit (IMU) is described. All inertial navigation systems consist of such IMU. The scalar calibration method is a base of the scalar method for quality monitoring and diagnostics. In accordance with scalar calibration method algorithms of fault diagnosis systems are developed. As a result of quality monitoring algorithm verification is implemented in the working capacity monitoring of IMU. A failure element determination is based on diagnostics algorithm verification and after that the reason for such failure is cleared. The process of verifications consists of comparison of the calculated estimations of biases, scale factor errors, and misalignments angles of sensors to their data sheet certificate, kept in internal memory of computer. As a result of such comparison the conclusion for working capacity of each IMU sensor can be made and also the failure sensor can be determined.

The Analysis on the Annual Change of Digital Aerial Camera's IMUs Boresight Misalignment

2021 ◽  
Vol 87 (11) ◽  
pp. 801-806
Author(s):  
Abdullah Kayı ◽  
Bülent Bayram ◽  
Dursun Zafer Şeker
Keyword(s):  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Annual Change ◽  
Short Term ◽  
Inertial Measurement ◽  
Term Stability ◽  
Long Term Stability ◽  
Flight Season

The system calibration determines the position and orientation between the sensor and the navigation systems, such as boresight misalignment. Although there is much research about boresight calibration, there are not sufficient studies on the frequency of the calibration performance. The short-term stability of boresight misalignment was investigated in previous studies, but long-term stability research could not be done. It is important to emphasize that long-term stability is still open to questions. In this study, an Ultracam Eagle digital aerial camera's data from 2012 to 2016 were analyzed and the question of how often calibration should be performed was investigated. Boresight misalignment does not remain constant on a yearly basis and should be calibrated every year before the flight season. It was observed that the boresight misalignment changed dramatically when the inertial measurement unit or camera was removed from the aircraft and sent to the manufacturer for factory calibration.

Integrated GNSS antenna with an embedded inertial measurement unit

2020 ◽  
pp. 16-23
Author(s):  
Nikolay N. Vasilyuk
Keyword(s):  
Inertial Measurement Unit ◽  
Data Exchange ◽  
Digital Data ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Inertial Measurement ◽  
Practical Applications ◽  
Integrated Antennas ◽  
Common Structure ◽  
Integrated Antenna

When constructing inertial/GNSS navigation systems, it is necessary to determine coordinates of a GNSS antenna relative to an inertial measurement unit. It is proposed to solve this problem by integrating of the inertial unit and GNSS antenna’s element into a common structure called an integrated antenna. This approach allows to determine the required coordinates in factory conditions, during a manufacturing of the integrated antenna. Operation principles of design modules of the integrated antenna and ways to use this antenna in the inertial/GNSS navigation systems have been described. Design features of a half-duplex digital data exchange between the antenna and a data processor have been indicated. Approaches to use this exchange to solve some service tasks of the navigation system have been proposed. It is noted that the integrated antenna has its own measuring basis. Methods of accounting of the attitude of this basis in practical applications of the integrated antennas in the single- and multi-antenna inertial/GNSS navigation systems have been described.

scholarly journals Performance Assessment of an Ultra Low-Cost Inertial Measurement Unit for Ground Vehicle Navigation

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3865 ◽  
Author(s):  
Rodrigo Gonzalez ◽  
Paolo Dabove
Keyword(s):  
Automotive Industry ◽  
Inertial Measurement Unit ◽  
Microelectromechanical Systems ◽  
Inertial Sensor ◽  
Low Cost ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Mass Market ◽  
Inertial Measurement ◽  
Mems Imu

Nowadays, navigation systems are becoming common in the automotive industry due to advanced driver assistance systems and the development of autonomous vehicles. The MPU-6000 is a popular ultra low-cost Microelectromechanical Systems (MEMS) inertial measurement unit (IMU) used in several applications. Although this mass-market sensor is used extensively in a variety of fields, it has not caught the attention of the automotive industry. Moreover, a detailed performance analysis of this inertial sensor for ground navigation systems is not available in the previous literature. In this work, a deep examination of one MPU-6000 IMU as part of a low-cost navigation system for ground vehicles is provided. The steps to characterize the performance of the MPU-6000 are divided in two phases: static and kinematic analyses. Besides, an additional MEMS IMU of superior quality is also included in all experiments just for the purpose of comparison. After the static analysis, a kinematic test is conducted by generating a real urban trajectory registering an MPU-6000 IMU, the higher-grade MEMS IMU, and two GNSS receivers. The kinematic trajectory is divided in two parts, a normal trajectory with good satellites visibility and a second part where the Global Navigation Satellite System (GNSS) signal is forced to be lost. Evaluating the attitude and position inaccuracies from these two scenarios, it is concluded in this preliminary work that this mass-market IMU can be considered as a convenient inertial sensor for low-cost integrated navigation systems for applications that can tolerate a 3D position error of about 2 m and a heading angle error of about 3 °.

Validity and Reliability of a New Device to Measure Type of Actions in Indoor Sports

2020 ◽  
Author(s):  
Carlos Lago-Fuentes ◽  
Paolo Aiello ◽  
Mauro Testa ◽  
Iker Muñoz ◽  
Marcos Mecías Calvo
Keyword(s):  
Coefficient Of Variation ◽  
Inertial Measurement Unit ◽  
High Reliability ◽  
Intraclass Correlation ◽  
Measurement Unit ◽  
Validity And Reliability ◽  
Inertial Measurement ◽  
New Device ◽  
Standing Up ◽  
Indoor Sports

AbstractThe purpose of this study was to analyze the validity and the reliability of the intensity ranges, number of actions and changes of direction measured by a commercial inertial measurement unit. Eleven elite youth futsal players performed a circuit with different type of displacements as sprinting, running at low-medium intensity, standing up and changes of direction. Data recorded by the Overtraq system were compared with video-analyzer during the six trials of each player. Standard error mean, Intraclass Correlation Coeficient and Coefficient of variation, were calculated to analyze the reliability of the device, as well as the Root Mean Square Error and Confidence Interval with correlation of Pearson for its validity. The results reported good validity for three intensity ranges (R2>0.7) with high reliability (Intraclass Correlation Coeficient: 0.8–0.9), especially for high intensity actions (Intraclass Correlation Coeficient: 0.95, Coefficient of Variation: 3.06%). Furthermore, the validity for the number of different actions was almost perfect (96.3–100%), with only small differences regarding changes of activity (mean error: 2.0%). The Overtraq system can be considered as a valid and reliable technology for measuring and monitoring actions at different intensities and changes of direction in futsal, likewise common actions for other indoor sports.

Error Modeling and Analysis of Inertial Measurement Unit Using Stochastic and Deterministic Techniques

2011 ◽  
Vol 403-408 ◽  
pp. 4447-4455
Author(s):  
Zeeshan Ashraf ◽  
Wajeeha Nafees
Keyword(s):  
Inertial Measurement Unit ◽  
Low Cost ◽  
Stochastic Approach ◽  
Error Modeling ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Modeling And Analysis ◽  
Inertial Measurement ◽  
Noise Parameters ◽  
Navigation Algorithm

The measurements provided by inertial measurement unit (IMU) are erroneous due to certain noise parameters which are needed to be taken into account because the corrupted data is of little practical value in inertial navigation systems (INS). By integrating the IMU data in navigation algorithm, these errors are accumulated, leading to significant drift in the attitude, position and velocity outputs. Several techniques have been devised for the error modeling of this error by way of Neural Networks (NNs), PSD, ARMA, etc. In this paper, the deterministic and stochastic approach is followed to model the noise parameters of a low cost IMU. The error parameters thus determined by using the both techniques help in the development of an effective navigation algorithm. Deterministic errors are calculated by the help of Up-Down Test and the Rate Table test. While the stochastic errors, which are more random in nature, are recognized using Power Spectral Density (PSD) Analysis and Allan Variance techniques.

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