Influence of Inertial Sensor Errors on GNSS/INS Integrated Navigation Performance

2016 ◽  
Author(s):  
Fang Hui ◽  
Feng Qin
Keyword(s):  
Inertial Sensor ◽  
Integrated Navigation ◽  
Sensor Errors

scholarly journals Tuning of the Wavelet Filters for the IMU Data Based on the PDC Method and the GPS Solution in a Bi-Dimensional Navigation Application

2019 ◽  
Vol 11 (3) ◽  
pp. 3-14
Author(s):  
Ioana Raluca ADOCHIEI ◽  
Teodor Lucian GRIGORIE ◽  
Felix Constantin ADOCHIEI
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Wavelet Filters ◽  
Time Frequency ◽  
Tuning Method ◽  
Coupling Dynamics ◽  
Navigation Accuracy ◽  
Sensor Errors

The degradation of navigation accuracy and integrity of GPS in the presence of radio frequency interference, hostile jamming and high dynamical situations, when the satellite signals may get lost due to signal blockage, led to the development of MEMS-INS/GPS integrated navigation systems for various applications of the positioning and navigation technologies. Unfortunately, the short-term advantages brought by the INS systems are overshadowed by their imprecise operation over the long term, mainly due to inertial sensor errors. A critical component of the inertial sensors errors is the noise. To improve the quality of the inertial sensors data, many denoising techniques have been used. Wavelet method has been proven as a useful tool for signal analysis, and it is widely used in signal processing and denoising applications. The here proposed technique is based on a time-frequency approach previously applied in bio-signals processing. In the proposed mechanism, the inertial sensors signals are processed analysed by using an extended version of the Wavelet transform. The optimal levels of decomposition are established for the wavelet filters, based on the evaluation of a parameter called coupling level (CL). It characterizes the coupling dynamics information between the reference signals, provided by a GPS, and the perturbed signal, which are the outputs of the inertial navigation system (INS). The proposed tuning method is experimentally tested in a bi-dimensional navigation application.

scholarly journals Analytical Assessment of the Propagation of Colored Sensor Noise in Strapdown Inertial Navigation

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6914
Author(s):  
Christopher Blum ◽  
Johann Dambeck
Keyword(s):  
Gaussian Noise ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
White Gaussian Noise ◽  
Sensor Data ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Sensor Noise ◽  
Sensor Errors ◽  
Strapdown Inertial Navigation

Knowledge of the propagation of sensor errors in strapdown inertial navigation is crucial for the design of inertial and integrated navigation systems. The propagation of initialization errors and deterministic sensor errors is well covered in the literature. If considered at all, the propagation of inertial sensor noise has typically been assessed for un-correlated (white) Gaussian noise. Real inertial sensor noise, however, is time-correlated (colored) and best described by a combination of different stochastic processes. In this paper, we demonstrate how a navigation system’s response to colored noise input differs from the response to bias-like or white noise inputs. We present a method for assessing the navigation error from various inertial sensor noise processes without the need for time-consuming Monte Carlo simulations and demonstrate its application and validity with real sensor data. The proposed method is used to determine in which scenarios the sensor’s real noise can be approximated by simple white Gaussian noise. The results indicate that neglecting colored sensor noise is justified for many applications, but should be checked individually for each sensor configuration and mission.

Optimising the Transfer Alignment of Weapon INS

2003 ◽  
Vol 56 (2) ◽  
pp. 323-335 ◽  
Author(s):  
Paul D. Groves
Keyword(s):  
Inertial Sensor ◽  
Integrated Navigation ◽  
Velocity Measurements ◽  
Transfer Alignment ◽  
Navigation Solution ◽  
Alignment Technique ◽  
Alignment Process ◽  
Using Data ◽  
Design Options ◽  
Better Than

Transfer alignment is the process of initialising and calibrating a weapon INS using data from the host aircraft's navigation system. To determine which transfer alignment technique performs best, different design options have been assessed, supported by simulation work. The dependence of transfer alignment performance on environmental factors, such as manoeuvres, alignment duration, lever arm and inertial sensor quality has also been studied. ‘Rapid’ alignment, using attitude as well as velocity measurements was found to perform better than ‘conventional’ techniques using only velocity. Innovative developments include the estimation of additional acceleration and gyro states and estimation of force dependent relative orientation, which has enabled robust alignment using wing rock manoeuvres, which do not require the pilot to change trajectory. Transfer alignment has been verified in real-time by flight trials on a Tornado aircraft. In addition, techniques have been developed to prevent transients in the aircraft integrated navigation solution following GPS re-acquisition after an outage of several minutes from disrupting the transfer alignment process.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

scholarly journals A Rapid Transfer Alignment Method for SINS Based on the Added Backward-Forward SINS Resolution and Data Fusion

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Data Fusion ◽  
Reference Data ◽  
Inertial Sensor ◽  
Sensor Data ◽  
Alignment Method ◽  
Initial Alignment ◽  
Transfer Alignment ◽  
Gyro Bias ◽  
Sensor Errors ◽  
Rapid Transfer

Two viewpoints are given: (1) initial alignment of strapdown inertial navigation system (SINS) can be fulfilled with a set of inertial sensor data; (2) estimation time for sensor errors can be shortened by repeated data fusion on the added backward-forward SINS resolution results and the external reference data. Based on the above viewpoints, aiming to estimate gyro bias in a shortened time, a rapid transfer alignment method, without any changes for Kalman filter, is introduced. In this method, inertial sensor data and reference data in one reference data update cycle are stored, and one backward and one forward SINS resolutions are executed. Meanwhile, data fusion is executed when the corresponding resolution ends. With the added backward-forward SINS resolution, in the above mentioned update cycle, the estimating operations for gyro bias are added twice, and the estimation time for it is shortened. In the ship swinging condition, with the “velocity plus yaw” matching, the effectiveness of this method is proved by the simulation.

scholarly journals Comparing Clothing-Mounted Sensors with Wearable Sensors for Movement Analysis and Activity Classification

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 82 ◽  
Author(s):  
Udeni Jayasinghe ◽  
William S. Harwin ◽  
Faustina Hwang
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Correlation Coefficients ◽  
Movement Analysis ◽  
Multiple Sensors ◽  
Sensor Orientation ◽  
Multiple Devices ◽  
Good Potential ◽  
Sensor Errors

Inertial sensors are a useful instrument for long term monitoring in healthcare. In many cases, inertial sensor devices can be worn as an accessory or integrated into smart textiles. In some situations, it may be beneficial to have data from multiple inertial sensors, rather than relying on a single worn sensor, since this may increase the accuracy of the analysis and better tolerate sensor errors. Integrating multiple sensors into clothing improves the feasibility and practicality of wearing multiple devices every day, in approximately the same location, with less likelihood of incorrect sensor orientation. To facilitate this, the current work investigates the consequences of attaching lightweight sensors to loose clothes. The intention of this paper is to discuss how data from these clothing sensors compare with similarly placed body worn sensors, with additional consideration of the resulting effects on activity recognition. This study compares the similarity between the two signals (body worn and clothing), collected from three different clothing types (slacks, pencil skirt and loose frock), across multiple daily activities (walking, running, sitting, and riding a bus) by calculating correlation coefficients for each sensor pair. Even though the two data streams are clearly different from each other, the results indicate that there is good potential of achieving high classification accuracy when using inertial sensors in clothing.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

scholarly journals A regression‐based methodology to improve estimation of inertial sensor errors using Allan variance data

Navigation ◽  
2019 ◽  
Vol 66 (1) ◽  
pp. 251-263 ◽  
Author(s):  
Juan Jurado ◽  
Christine M. Schubert Kabban ◽  
John Raquet
Keyword(s):  
Inertial Sensor ◽  
Allan Variance ◽  
Sensor Errors
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