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Performance Analysis of Alignment Process of MEMS IMU  [PDF]
Vadim Bistrov
International Journal of Navigation and Observation , 2012, DOI: 10.1155/2012/731530
Abstract: The procedure of determining the initial values of the attitude angles (pitch, roll, and heading) is known as the alignment. Also, it is essential to align an inertial system before the start of navigation. Unless the inertial system is not aligned with the vehicle, the information provided by MEMS (microelectromechanical system) sensors is not useful for navigating the vehicle. At the moment MEMS gyroscopes have poor characteristics and it’s necessary to develop specific algorithms in order to obtain the attitude information of the object. Most of the standard algorithms for the attitude estimation are not suitable when using MEMS inertial sensors. The wavelet technique, the Kalman filter, and the quaternion are not new in navigation data processing. But the joint use of those techniques for MEMS sensor data processing can give some new results. In this paper the performance of a developed algorithm for the attitude estimation using MEMS IMU (inertial measurement unit) is tested. The obtained results are compared with the attitude output of another commercial GPS/IMU device by Xsens. The impact of MEMS sensor measurement noises on an alignment process is analysed. Some recommendations for the Kalman filter algorithm tuning to decrease standard deviation of the attitude estimation are given. 1. Introduction Navigation can be defined as the process of determining the position, orientation, and velocity of an object. A GPS-based navigation is quick and drift free and is readily available most of the time. However, as the GPS requires direct line of sight signals from at least four GPS satellites, the navigation can be frequently interrupted in the land based applications. The GPS signal gets lost due to various factors such as the blockage by buildings, trees, and other natural and nonnatural obstructions. This affects both the amplitude and phase of the received satellite signals and causes the receiver to lose lock on the blocked satellite, meaning that it needs both to reacquire the signal and to resolve the ambiguities in the phase measurements. Both these processes take time, and if there are several satellites affected, the receiver cannot provide a position solution for a significant period of time. Also it is worth mentioning that the data rate for the GPS can be too low for the particular application. In such situations when the GPS signals are not available, a relative navigation can be performed using the inertial sensors (accelerometers and gyroscopes) and magnetometers. The strapdown inertial navigation system (SINS) has been widely used in
捷联惯导系统最简多位置解析对准
The simplest multi-position analytic alignment for SINS
 [PDF]

谭彩铭,王宇,苏岩,朱欣华
- , 2015, DOI: 10.13700/j.bh.1001-5965.2015.0033
Abstract: 摘要 传统的多位置解析对准方法一般要求将捷联惯导系统(SINS)安装在一个伺服平台上并绕天向轴旋转90°或180°,这对工程带来不便,且伺服平台的精度会影响多位置解析对准的精度.针对这一问题,提出最简多位置解析对准方法,指出任意两位置是实现SINS多位置解析对准所需的最小条件,即通常理论上任意两位置可解算出惯性测量单元(IMU)的常值偏置,给出了计算方法,并通过仿真实例加以说明和验证,可以作为一种简易初始对准或现场标定方法.另外通过解析方法指出在特殊姿态下,某单一轴向的加速度计常值偏置或陀螺常值漂移可以直接被较好地估计出来,结论可用于进一步改进多位置对准方法.
Abstract:The strapdown inertial navigation system (SINS) need to be installed on a servo platform and rotated through 90 ° or 180 ° about the up axis for traditional multi-position analytic alignment method. Thus it will bring inconvenience, and the precision of the servo platform will directly affect the precision of the multi-position analytic alignment method. To address the issue, a simplest multi-position analytic alignment method was proposed. The multi-position analytic alignment can be done by any two positions, or to say, the constant biases of the inertial measurement unit (IMU) can be ordinarily obtained through the information in any two positions. The computation procedure of this method was given and simulations proved the validity of this method. The simplest multi-position analytic alignment with any two positions can be used as a simple initial alignment method or a field calibration method. Moreover, it is proposed that the constant bias of the accelerometer or the constant drift of the gyroscope in a certain axis can be well estimated when the IMU stays in some particular attitudes. The conclusion can be used for further improvement of the multi-position alignment method.
光纤陀螺惯导系统扰动条件下的初始对准
Initial Alignment of Fiber Optic Gyroscope SINS with Disturbances
 [PDF]

张环, 黄湘远, 程旭维
Dynamical Systems and Control (DSC) , 2013, DOI: 10.12677/DSC.2013.21002
Abstract:
针对装甲车辆上在发动机不关闭及有乘员走动、上下车等扰动条件下的光纤陀螺捷联惯导系统(SINS)初始对准技术进行了相关研究。首先通过分析SINS惯性元件的信号特征基于小波分析理论对观测信号进行降噪预处理降低外界干扰的影响;采用自适应卡尔曼滤波完成精对准过程,使之具有更好的稳定性。仿真实验表明该算法能有效提高扰动基础上的对准稳定性及精度,具有较大的工程应用价值。
The initial alignment of fiber optic gyroscope strap-down inertial navigation system (SINS) for armored ve- hicles is studied when disturbance are present, for example when the motor is running on or people are walking on the vehicle. First, based on the signal analyses of the SINS inertial instruments, the observation signals were preprocessed by wavelet transform to reduce the interference, and then the adaptive Kalman filter algorithm was improved to increase the stability of alignment. The experimental results show that this technique effectively increases the alignment accu- racy with disturbances, and the application value is obvious.
Gyrocompass Alignment Method of Sins Based on Kalman Filtering Pretreatment and Dynamic Gain Adjustment on a Rocking Base  [PDF]
Long-Hua Ma,Kai-Li Wang,Hui Li
Information Technology Journal , 2013,
Abstract: Land combat vehicles are inevitably subject to the vibration disturbance by wind gust or engine idling, etc. in the stationary initial alignment process of the Strapdown Inertial Navigation System (SINS). Obviously, it’s necessary to consider the impact of vibration disturbance during the alignment process to achieve better performance. In order to guarantee the alignment accuracy on the rocking base and shorten the convergence time of alignment, a gyrocompass alignment method of SINS based on Kalman filter pretreatment and dynamic gain adjustment was proposed. The output of gyros and accelerometers was firstly pre-filtered by Kalman filter to remove the impact of high-frequency small-amplitude rocking interference. The low-frequency large-amplitude rocking interference on vehicle was tracked through dynamic gain adjustment of gyrocompass alignment. The vehicle test of a ring laser SINS showed that the new gyrocompass alignment method can suppress high-frequency disturbances when the vehicle underwent low-frequency large-amplitude rocking interference. And the alignment process can track the attitude change of vehicle caused by low-frequency large-amplitude rocking interference. Comparing with traditional gyrocompass alignment algorithm and Kalman filter alignment method, the performance of the new gyrocompass alignment method is much improved by filtering random noise caused by vibration disturbance of vehicle effectively.
A fast and accurate initial alignment method for strapdown inertial navigation system on stationary base

Xinlong WANG,Gongxun SHEN,

控制理论与应用 , 2005,
Abstract: In this work,a fast an d accurate stationary alignment method for strapdown inertial navigation system (SINS) is proposed.It h as been demonstrated that the stationary alignment o f SINS can be improved by employing the multipositio n technique,but the alignment time of the azimuth error is relatively longer.Over here,the two-posi tion alignment principle is presented.On the basis of this SINS error model,a fast estimation algorithm of the azimuth error for the initial a lignment of SINS on stationary base is derived f ully from the horizontal velocity outputs and the output rates,and the novel azimuth error estimatio n algorithm is used for the two-position alignment. Consequently,the speed and accuracy of the SINS' s initial alignment is enhanced greatly.The computer simulation results illustrate the efficiency of this alignment method.
A Novel Scheme for DVL-Aided SINS In-Motion Alignment Using UKF Techniques  [PDF]
Wanli Li,Jinling Wang,Liangqing Lu,Wenqi Wu
Sensors , 2013, DOI: 10.3390/s130101046
Abstract: In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU  [PDF]
Francesco Montorsi,Fabrizio Pancaldi,Giorgio M. Vitetta
Computer Science , 2015,
Abstract: Inertial navigation systems for pedestrians are infrastructure-less and can achieve sub-meter accuracy in the short/medium period. However, when low-cost inertial measurement units (IMU) are employed for their implementation, they suffer from a slowly growing drift between the true pedestrian position and the corresponding estimated position. In this paper we illustrate a novel solution to mitigate such a drift by: a) using only accelerometer and gyroscope measurements (no magnetometers required); b) including the sensor error model parameters in the state vector of an extended Kalman filter; c) adopting a novel soft heuristic for foot stance detection and for zero-velocity updates. Experimental results evidence that our inertial-only navigation system can achieve similar or better performance with respect to pedestrian dead-reckoning systems presented in related studies, although the adopted IMU is less accurate than more expensive counterparts.
GPS/Low-Cost IMU/Onboard Vehicle Sensors Integrated Land Vehicle Positioning System  [cached]
Gao Jianchen,Petovello MarkG,Cannon MElizabeth
EURASIP Journal on Embedded Systems , 2007,
Abstract: This paper aims to develop a GPS, low-cost IMU, and onboard vehicle sensors integrated land vehicle positioning system at low cost and with high (cm level) accuracy. Using a centralized Kalman filter, the integration strategies and algorithms are discussed. A mechanism is proposed for detecting and alleviating the violation of the lateral nonholonomic constraint on the wheel speed sensors that is widely used in previous research. With post-mission and real-time tests, the benefits gained from onboard vehicle sensors and the side slip detection and alleviation mechanism in terms of the horizontal positioning accuracy are analyzed. It is illustrated by all the tests that GPS plays a dominant role in determining the absolute positioning accuracy of the system when GPS is fully available. The integration of onboard vehicle sensors can improve the horizontal positioning accuracy during GPS outages. With respect to GPS and low-cost IMU integrated system, the percentage improvements from the wheel speed sensor are 90.4% for the open-sky test and 56.0% for suburban area real-time test. By integrating all sensors to detect and alleviate the violation of the lateral nonholonomic constraint, the percentage improvements over GPS and low-cost IMU integrated system can be enhanced to 92.6% for open-sky test and 65.1% for the real-time test in suburban area.
GPS/Low-Cost IMU/Onboard Vehicle Sensors Integrated Land Vehicle Positioning System  [cached]
Jianchen Gao,Mark G. Petovello,M. Elizabeth Cannon
EURASIP Journal on Embedded Systems , 2007, DOI: 10.1155/2007/62616
Abstract: This paper aims to develop a GPS, low-cost IMU, and onboard vehicle sensors integrated land vehicle positioning system at low cost and with high (cm level) accuracy. Using a centralized Kalman filter, the integration strategies and algorithms are discussed. A mechanism is proposed for detecting and alleviating the violation of the lateral nonholonomic constraint on the wheel speed sensors that is widely used in previous research. With post-mission and real-time tests, the benefits gained from onboard vehicle sensors and the side slip detection and alleviation mechanism in terms of the horizontal positioning accuracy are analyzed. It is illustrated by all the tests that GPS plays a dominant role in determining the absolute positioning accuracy of the system when GPS is fully available. The integration of onboard vehicle sensors can improve the horizontal positioning accuracy during GPS outages. With respect to GPS and low-cost IMU integrated system, the percentage improvements from the wheel speed sensor are 90.4% for the open-sky test and 56.0% for suburban area real-time test. By integrating all sensors to detect and alleviate the violation of the lateral nonholonomic constraint, the percentage improvements over GPS and low-cost IMU integrated system can be enhanced to 92.6% for open-sky test and 65.1% for the real-time test in suburban area.
Optimization-based Alignment for Strapdown Inertial Navigation System Comparison and Extension  [PDF]
Lubin Chang,Jingshu Li,Kailong Li
Computer Science , 2014,
Abstract: In this paper, the optimization-based alignment (OBA) methods are investigated with main focus on the vector observations construction procedures for the strapdown inertial navigation system (SINS). The contributions of this study are twofold. First the OBA method is extended to be able to estimate the gyroscopes biases coupled with the attitude based on the construction process of the existing OBA methods. This extension transforms the initial alignment into an attitude estimation problem which can be solved using the nonlinear filtering algorithms. The second contribution is the comprehensive evaluation of the OBA methods and their extensions with different vector observations construction procedures in terms of convergent speed and steady-state estimate using field test data collected from different grades of SINS. This study is expected to facilitate the selection of appropriate OBA methods for different grade SINS.
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