The Effectiveness of Acceleration Matching According to the Sensor Performance in Shipboard Rapid Transfer Alignment

2019 ◽  
Vol 73 (1) ◽  
pp. 1-15
Author(s):  
Hojin Ju ◽  
Seong Yun Cho ◽  
Chan Gook Park
Keyword(s):  
Angular Velocity ◽  
Navigation System ◽  
Performance Index ◽  
Inertial Navigation System ◽  
The Other ◽  
Transfer Alignment ◽  
Sensor Performance ◽  
Velocity Information ◽  
Simulation Results ◽  
Rapid Transfer

In this study, the effect of acceleration matching according to sensor specifications in rapid transfer alignment is analysed. In general, the velocity and attitude information of the Master Inertial Navigation System (MINS) is used for transfer alignment. MINS angular velocity information is used to improve the alignment speed in shipboard transfer alignment. Acceleration matching, on the other hand, is generally considered an impractical option for transfer alignment. However, in the case of shipboard transfer alignment, acceleration matching is thought to be effective. In order to analyse the performance of acceleration matching, a performance index is defined and the efficiency of acceleration matching is analysed according to various sensor specifications and simulation environments. Based on the analysis of the estimated performance according to the simulation results, it is confirmed that acceleration matching in rapid transfer alignment is valid.

Transfer Alignment for MEMS Integrated Navigation System Based on H∞ Filter

2014 ◽  
Vol 490-491 ◽  
pp. 886-890
Author(s):  
Xing Zhi Zhang ◽  
Kun Peng He ◽  
Chen Yang Wang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Kalman Filters ◽  
Inertial Navigation System ◽  
Digital Simulation ◽  
Integrated Navigation ◽  
Misalignment Angle ◽  
Transfer Alignment ◽  
Integrated Navigation System ◽  
Simulation Results

The transfer alignment of strapdown inertial units were proposed that use the H filter to estimate the misalignment of the slave INS (inertial navigation system) relative to the master INS. Characteristics of the H filter in transfer alignment were studied in detail by checking digital simulation results obtained by using the H and Kalman filters. The results shows that the misalignment angle obtained with the H filter converge faster and closer to the exact values than do those obtained with the Kalman filter. The H filter is more robust than the Kalman filter in transfer alignment for MEMS integrated navigation system.

scholarly journals Rapid Transfer Alignment of SINS with Measurement Packet Dropping based on a Novel Suboptimal Estimator

2019 ◽  
Vol 69 (4) ◽  
pp. 320-327
Author(s):  
Hongde Dai ◽  
Juan Li ◽  
Liang Tang ◽  
Xibin Wang
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Performance Comparison ◽  
Measurement Equation ◽  
Packet Dropping ◽  
Transfer Alignment ◽  
Moving Base ◽  
Independent Identically Distributed ◽  
Rapid Transfer

Transfer alignment (TA) is an important step for strapdown inertial navigation system (SINS) starting from a moving base, which utilises the information proposed from the higher accurate and well performed master inertial navigation system. But the information is often delayed or even lost in real application, which will seriously affect the accuracy of TA. This paper models the stochastic measurement packet dropping as an independent identically distributed (IID) Bernoulli random process, and introduces it into the measurement equation of rapid TA, and the influence of measurement packet dropping is analysed. Then, it presents a suboptimal estimator for the estimation of the misalignment in TA considering the random arrival of the measurement packet. Simulation has been done for the performance comparison about the suboptimal estimator, standard Kalman filter and minimum mean squared estimator. The results show that the suboptimal estimator has better performance, which can achieve the best TA accuracy.

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.

Screw Algorithm Optimized with Instantaneous Signals

2012 ◽  
Vol 229-231 ◽  
pp. 1671-1674
Author(s):  
Jian Feng Chen ◽  
Xi Yuan Chen ◽  
Xue Fen Zhu
Keyword(s):  
Navigation System ◽  
Inertial Navigation System ◽  
Angular Rate ◽  
Inertial Navigation ◽  
Strapdown Inertial Navigation System ◽  
Specific Force ◽  
Dual Quaternion ◽  
Screw Motion ◽  
Direct Component ◽  
Simulation Results

Recent dramatic progress in strapdown inertial navigation system (SINS) algorithm is the design of SINS principle based on screw algorithm, utilizing dual quaternion. In this paper, the screw algorithm consisting of angular rate and specific force is optimized under a special screw motion. The special screw motion is derived from classical screw motion and can be taken as a complicated sculling motion including classical coning motion. Subsequently, the coefficients in the multi-sample screw algorithms and the corresponding algorithm drifts are determined by minimizing the error on direct component. The simulation results of attitude and velocity errors agree with the optimization goals, except when the number of subinterval is greater than 2. An explanation of this phenomenon is delivered.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

1974 TEST OF LACOSTE AND ROMBERG INERTIAL NAVIGATION SYSTEM

Geophysics ◽  
1977 ◽  
Vol 42 (3) ◽  
pp. 594-601 ◽  
Author(s):  
Lucien J. B. LaCoste
Keyword(s):  
Gulf Of Mexico ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Satellite Navigation ◽  
The Other ◽  
Inertial System ◽  
Oil Exploration ◽  
Satellite Information ◽  
Made In

In mid-1974 a test was made in the Gulf of Mexico of a LaCoste and Romberg inertial system for the measurement of the Eötvös correction for shipboard gravity meters. Since the system is designed for operation with satellite navigation, inertial velocities were updated at 2- to 3-hour intervals, using Lorac information because no satellite information was available. Two types of comparison were made between the inertial and Lorac data. One comparison was the rms difference between results from the two methods—0.46 knot, which corresponds to 3.0 mgal at a latitude of 30 degrees. The other comparison related to noise which could be mistaken for anomalies of interest in oil exploration. The comparison indicated that such noise in the inertial data was only about one third that in the Lorac data.

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