In-motion Alignment Algorithm for Vehicle Carried SINS Based on Odometer Aiding

2017 ◽  
Vol 70 (6) ◽  
pp. 1349-1366 ◽  
Author(s):  
Haijian Xue ◽  
Xiaosong Guo ◽  
Zhaofa Zhou ◽  
Kunming Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Measured Data ◽  
The Body ◽  
Alignment Algorithm ◽  
Alignment Method ◽  
Initial Angle ◽  
Body Frame ◽  
Measurement Equation

In-motion alignment plays an important role in improving the manoeuvring capability of a vehicle, and allows the initialisation of a Strapdown Inertial Navigation System (SINS) while moving. Odometer (OD) aided in-motion alignment is widely adopted owing to its fully self-contained characteristic. This paper proposes a complete in-motion alignment algorithm for a vehicle-carried SINS based on odometer aiding, in which an in-motion coarse alignment method using the integration form of the velocity update equation in the body frame to give a rough initial angle is introduced and a new measurement equation in the body frame with a Kalman filter (KF) for the in-motion fine alignment is established. The advantages of the proposed method are verified by simulation and measured data.

An Improved Initial Alignment Method of Strap-Down Inertial Navigation System on a Swaying Base

2013 ◽  
Vol 415 ◽  
pp. 143-148
Author(s):  
Li Hua Zhu ◽  
Xiang Hong Cheng
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Alignment Algorithm ◽  
Alignment Method ◽  
Initial Alignment ◽  
Vehicle Simulation ◽  
Robust Property ◽  
Simulation Results ◽  
Lever Arm Effect ◽  
The Impact

The design of an improved alignment method of SINS on a swaying base is presented in this paper. FIR filter is taken to decrease the impact caused by the lever arm effect. And the system also encompasses the online estimation of gyroscopes’ drift with Kalman filter in order to do the compensation, and the inertial freezing alignment algorithm which helps to resolve the attitude matrix with respect to its fast and robust property to provide the mathematical platform for the vehicle. Simulation results show that the proposed method is efficient for the initial alignment of the swaying base navigation system.

Self-Alignment Algorithm for Strapdown Inertial Navigation System under Strong Flurry Interference

2013 ◽  
Vol 347-350 ◽  
pp. 3667-3671 ◽  
Author(s):  
Yue Gang Wang ◽  
Jia Sheng Yang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
The Body ◽  
Alignment Algorithm ◽  
Result Show ◽  
Linear Movement ◽  
Initial Attitude ◽  
Strapdown Inertial Navigation

For the strong flurry interrupting, the body will suffer large swaying motion when it is in erecting state ,the output of its strapdown inertial navigation system (SINS) will be disturbed for the high gravitational. center of IMU, the conventional methods are difficult to achieve alignment rapidly and accurately, to solve this problem, an anti-interference self-alignment algorithm for SINS which under strong flurry is presented, which utilizes the continuous attitude update in inertial reference frame to record the attitude changes caused by sway interrupt to remove the angular interrupting, and uses the characteristics that the body exists a shake center whose speed is zero to remove the linear movement interrupting by acquiring the equivalent specific force of the shake center, and then uses the estimation of the initial attitude to determinate the attitude of the body. The simulation result show that the presented algorithm can accomplish alignment quickly even in the presence of strong flurry interference without coarse alignment phase.

scholarly journals A Method for SINS Alignment with Large Initial Misalignment Angles Based on Kalman Filter with Parameters Resetting

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xixiang Liu ◽  
Xiaosu Xu ◽  
Yiting Liu ◽  
Lihui Wang
Keyword(s):  
Kalman Filter ◽  
Covariance Matrix ◽  
Nonlinear Problem ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Error Model ◽  
The State ◽  
Alignment Method ◽  
Initial Alignment ◽  
Alignment Process

In the initial alignment process of strapdown inertial navigation system (SINS), large initial misalignment angles always bring nonlinear problem, which causes alignment failure when the classical linear error model and standard Kalman filter are used. In this paper, the problem of large misalignment angles in SINS initial alignment is investigated, and the key reason for alignment failure is given as the state covariance from Kalman filter cannot represent the true one during the steady filtering process. According to the analysis, an alignment method for SINS based on multiresetting the state covariance matrix of Kalman filter is designed to deal with large initial misalignment angles, in which classical linear error model and standard Kalman filter are used, but the state covariance matrix should be multireset before the steady process until large misalignment angles are decreased to small ones. The performance of the proposed method is evaluated by simulation and car test, and the results indicate that the proposed method can fulfill initial alignment with large misalignment angles effectively and the alignment accuracy of the proposed method is as precise as that of alignment with small misalignment angles.

scholarly journals A new polar alignment algorithm based on the Huber estimation filter with the aid of BeiDou Navigation Satellite System

2021 ◽  
Vol 17 (3) ◽  
pp. 155014772110041
Author(s):  
Bin Zhao ◽  
Qinghua Zeng ◽  
Jianye Liu ◽  
Chunlei Gao ◽  
Tianyu Zhao
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Satellite System ◽  
Estimation Accuracy ◽  
Polar Regions ◽  
Alignment Algorithm ◽  
Navigation Satellite ◽  
Beidou Navigation Satellite System ◽  
Non Gaussian

For aircrafts equipped with BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated navigation system, BeiDou Navigation Satellite System information can be used to achieve autonomous alignment. However, due to the complex polar environment and multipath effect, BeiDou Navigation Satellite System measurement noise often exhibits a non-Gaussian distribution that will severely degrade the estimation accuracy of standard Kalman filter. To address this problem, a new polar alignment algorithm based on the Huber estimation filter is proposed in this article. Considering the special geographical conditions in the polar regions, the dynamic model and the measurement model of BeiDou Navigation Satellite System/Strapdown Inertial Navigation System integrated alignment system in the grid frame are derived in this article. The BeiDou Navigation Satellite System measurement noise characteristics in the polar regions are analyzed and heavy-tailed characteristics are simulated, respectively. Since the estimation accuracy of standard Kalman filter can be severely degraded under non-Gaussian noise, a Kalman filter based on the Huber estimation is designed combining grid navigation system and generalized maximum likelihood estimation. The simulation and experiment results demonstrate that the proposed algorithm has better robustness under non-Gaussian noise, and it is effective in the polar regions. By employing the proposed algorithm, the rapidity and accuracy of the alignment process can be improved.

Kalman Filter Design for Initial Precision Alignment of a Strapdown Inertial Navigation System on a Rocking Base

2014 ◽  
Vol 68 (1) ◽  
pp. 184-195 ◽  
Author(s):  
Hanzhou Li ◽  
Quan Pan ◽  
Xiaoxu Wang ◽  
Xiangjun Jiang ◽  
Lin Deng
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Filter Design ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Alignment Algorithm ◽  
Attitude Tracking ◽  
Alignment Process ◽  
Strapdown Inertial Navigation

In this paper, a conventional Strapdown Inertial Navigation System (SINS) alignment method on a disturbed base is analysed. A novel method with an attitude tracking idea is proposed for the rocking base alignment. It is considered in this method that the alignment algorithm should track the rocking base attitude real changes in the alignment process, but not excessively restrain disturbance. According to this idea, a rapid alignment algorithm is devised for the rocking base. In the algorithm, coarse alignment is carried out within 30 s in the inertial frame with alignment precision less than 2°, which meets Kalman filter linearization conditions well. Then a Kalman filter with ten state vectors and four measurement vectors is applied for the fine alignment to improve the capability of the algorithm in tracking the vehicle attitude. A turntable rotation experiment is carried out to validate the capability of the fine algorithm in tracing the large magnitude change during alignment. It is shown that the repeated alignment precision is about 0·04° by the alignment experiment on a rocking vehicle, with alignment time of 180 s. The Laser Strapdown Inertial Navigation System (LINS) ground navigation experiment suggests that the algorithm proposed by this paper can be satisfied without the need of high precision SINS alignment.

Integration of the inertial navigation system with consecutive images of a camera by relative position and attitude updating

2019 ◽  
Vol 233 (15) ◽  
pp. 5592-5605
Author(s):  
Habib Ghanbarpour Asl ◽  
Abbas Dehghani Firouzabadi
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Unscented Kalman Filter ◽  
Inertial Navigation ◽  
New Method ◽  
Measurement Unit ◽  
Measurement Equation ◽  
Implicit Measurement ◽  
System Errors

This paper introduces a new method for improving the inertial navigation system errors using information provided by the camera. An unscented Kalman filter is used for integrating the inertial measurement unit data with the features’ constraints extracted from the camera’s image. The constraints, in our approach, comprise epipolar geometry of two consecutive images with more than 65% coverage. Tracking down a known feature in two consecutive images results in emergence of stochastic epipolar constraint. It emerges in the form of an implicit measurement equation of the Kalman filter. Correctly matching features of the two images is necessary for reducing the navigation system errors because they act as external information for the inertial navigation system. A new method has been presented in this study based on the covariance analysis of the matched feature rays’ intersection points on the ground, which sieves the false matched features. Then, the inertial navigation system and matched feature information is integrated through the unscented Kalman filter filter and the states of the vehicle (attitude, position, and velocity) are corrected according to the last image. In this paper, the relative navigation parameters against the absolute one have been corrected. To avoid increasing dimensions of the covariance matrix, sequential updating procedure is used in the measurement equation. The simulation results show good performance of the proposed algorithm, which can be easily utilized for real flights.

scholarly journals An Improved ACKF/KF Initial Alignment Method for Odometer-Aided Strapdown Inertial Navigation System

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3896 ◽  
Author(s):  
Kang Gao ◽  
Shunqing Ren ◽  
Guoxing Yi ◽  
Jiapeng Zhong ◽  
Zhenhuan Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Observation Equation ◽  
Alignment Method ◽  
Misalignment Angle ◽  
Initial Alignment ◽  
Error Equation

For a land-vehicle strapdown inertial navigation system (SINS), the problem of initial alignment with large misalignment angle in-motion needs to be solved urgently. This paper proposes an improved ACKF/KF initial alignment method for SINS aided by odometer. The SINS error equation with large misalignment angle is established first in the form of an Euler angle. The odometer/gyroscope dead reckoning (DR) error equation is deduced, which makes the observation equation linear when the position is taken as the observation of the Kalman filter. Then, based on the cubature Kalman filter, the Sage-Husa adaptive filter and the characteristics of the observation equation, an improved ACKF/KF method is proposed, which can accomplish initial alignment well in the case of unknown measurement noise. Computer simulation results show that the performance of the proposed ACKF/KF algorithm is superior to EKF, CKF and AEKF method in accuracy and stability, and the vehicle test validates its advantages.

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