A Unified Nonsingular Rapid Transfer Alignment Solution for Tactical Weapon Based on Matrix Kalman Filter

Study on a Twice Transfer Alignment Based on Dual Model

Discrete Dynamics in Nature and Society ◽

10.1155/2015/943065 ◽

2015 ◽

Vol 2015 ◽

pp. 1-5

Author(s):

Shuai Chen ◽

Runwu Zhong ◽

Xiaohui Liu ◽

Ahmed Alsaedi

Keyword(s):

Kalman Filter ◽

Convergence Time ◽

Alignment Algorithm ◽

Dual Model ◽

Matching Method ◽

Error Models ◽

Transfer Alignment ◽

Nonlinear Error ◽

Dual Models ◽

Rapid Transfer

This paper proposes a twice rapid transfer alignment algorithm based on dual models in order to solve the problems such as long convergence time, poor accuracy, and heavy computation burden resulting from the traditional nonlinear error models. The quaternion matching method based on quaternion error model along with the extended Kalman filter (EKF) is applied to deal with the large misalignment in the first phase. Then in the second transfer alignment phase, velocity plus attitude matching method as well as classical Kalman filter is adopted. The simulation and the results of vehicle tests demonstrate that this method combines the advantages of both nonlinear and linear error models with the guarantee of accuracy and fastness.

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Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

Sensors ◽

10.3390/s17010152 ◽

2017 ◽

Vol 17 (12) ◽

pp. 152 ◽

Cited By ~ 11

Author(s):

Hairong Chu ◽

Tingting Sun ◽

Baiqiang Zhang ◽

Hongwei Zhang ◽

Yang Chen

Keyword(s):

Kalman Filter ◽

Transfer Alignment ◽

Rapid Transfer

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Rapid transfer alignment based on unscented Kalman filter

2006 American Control Conference ◽

10.1109/acc.2006.1656548 ◽

2006 ◽

Cited By ~ 7

Author(s):

Yanling Hao ◽

Zhilan Xiong ◽

Wei Wang ◽

Feng Sun

Keyword(s):

Kalman Filter ◽

Unscented Kalman Filter ◽

Transfer Alignment ◽

Rapid Transfer

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Analysis of Three Velocity Plus Attitude Matching Methods for Transfer Alignment

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.433-440.2802 ◽

2012 ◽

Vol 433-440 ◽

pp. 2802-2807

Author(s):

Ying Hong Han ◽

Wan Chun Chen

Keyword(s):

Kalman Filter ◽

Inertial Navigation ◽

Navigation Systems ◽

Misalignment Angle ◽

Inertial Navigation Systems ◽

Excellent Performance ◽

Transfer Alignment ◽

Matching Methods ◽

Moving Base

For inertial navigation systems (INS) on moving base, transfer alignment is widely applied to initialize it. Three velocity plus attitude matching methods are compared. And Kalman filter is employed to evaluate the misalignment angle. Simulations under the same conditions show which scheme has excellent performance in precision and rapidness of estimations.

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

Complexity ◽

10.1155/2018/3039061 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 4

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.

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