scholarly journals The Design and Implementation of a Vehicle Mounted Inertial Navigation System Based on GPS/MEMS Inertial Sensor

2017 ◽  
Vol 05 (01) ◽  
pp. 1-9
Author(s):  
孝兵 张
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Design And Implementation

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.

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.

Optimisation-based Transfer Alignment and Calibration Method for Inertial Measurement Vector Integration Matching

2016 ◽  
Vol 70 (3) ◽  
pp. 595-606 ◽  
Author(s):  
Lili Xie ◽  
Jiazhen Lu
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Prior Information ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Inertial Measurement ◽  
Transfer Alignment ◽  
Sensor Error ◽  
Vector Integration ◽  
Measurement Vector

The traditional Kalman filtering-based transfer alignment methods largely depend on prior information for initialisation. However, the initialisation process is hard to fulfil on a moving base. In this paper, a type of inertial measurement vector integration matching for optimisation-based transfer alignment and calibration is proposed to estimate the misalignment between the Master Inertial Navigation System (MINS) and Slave Inertial Navigation System (SINS), and main inertial sensor error parameters of SINS, including bias and scale factor error. In contrast to filter techniques, the proposed method has the capability of self-initialisation and provides a new idea to solve the alignment and calibration problem. No prior information is needed. Moreover, the integration time interval for the matching inertial measurement vector is selected by considering both the observation degree of inertial sensor error parameters and the noise effect. Simulation results demonstrate that the proposed method has faster convergence and is more accurate than Kalman filter techniques.

Research on Micro Inertial Sensor Error Compensation Technology in SINS

2013 ◽  
Vol 273 ◽  
pp. 532-536
Author(s):  
Jian Ming Zhang ◽  
Xing Hui Zhang
Keyword(s):  
Measurement Error ◽  
Navigation System ◽  
Error Compensation ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Calculation Error ◽  
Pretreatment Technology ◽  
Mathematical Derivation ◽  
Accelerometer Measurement

The micro inertial sensor parts (gyroscope and accelerometer) measurement error is the main factors to influence the accuracy of the inertial navigation system, in order to improve the navigation precision of SINS (strap-down inertial navigation system), we must reduce inertial sensor measurement error. Based on the error analysis of the inertial measurement device, with accelerometer attitude computation information as gyro attitude computation information reference quantity, established the algorithm of the attitude angle. In the sensor output signal pretreatment technology, this paper mainly introduced the use of surface fitting technology, to establish the error compensation surface equation, and gives the mathematical derivation of calculation error model parameter, the experiment got a good compensation effect.

Compensation for Stochastic Error of Gyros in a Dual-axis Rotational Inertial Navigation System

2015 ◽  
Vol 69 (1) ◽  
pp. 169-182 ◽  
Author(s):  
Zhichao Zheng ◽  
Songlai Han ◽  
Jin Yue ◽  
Linglong Yuan
Keyword(s):  
Navigation System ◽  
High Performance ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Tests ◽  
Stochastic Error ◽  
Stochastic Errors

A dual-axis rotational Inertial Navigation System (INS) has received wide attention in recent years because of high performance and low cost. However, some errors of inertial sensors such as stochastic errors are not averaged out automatically during navigation. Therefore a Twice Position-fix Reset (TPR) method is provided to enhance accuracy of a dual-axis rotational INS by compensating stochastic errors. According to characteristics of an azimuth error introduced by stochastic errors of an inertial sensor in the dual-axis rotational INS, both an azimuth error and a radial-position error are much better corrected by the TPR method based on an optimised error propagation equation. As a result, accuracy of the dual-axis rotational INS is prominently enhanced by the TPR method, as is verified by simulations and field tests.

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