scholarly journals A Kalman Filter for SINS Self-Alignment Based on Vector Observation

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 264 ◽  
Author(s):  
Xiang Xu ◽  
Xiaosu Xu ◽  
Tao Zhang ◽  
Yao Li ◽  
Jinwu Tong
Keyword(s):  
Kalman Filter ◽  
Vector Observation

scholarly journals Linear Kalman Filter for Attitude Estimation from Angular Rate and a Single Vector Measurement

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Shangqiu Shan ◽  
Zhongxi Hou ◽  
Jin Wu
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Angular Rate ◽  
Attitude Estimation ◽  
Estimation Accuracy ◽  
Kinematic Equation ◽  
Vector Measurement ◽  
Vector Observation ◽  
Execution Speed ◽  
External Acceleration

In this paper, a new Kalman filtering scheme is designed in order to give the optimal attitude estimation with gyroscopic data and a single vector observation. The quaternion kinematic equation is adopted as the state model while the quaternion of the attitude determination from a strapdown sensor is treated as the measurement. Derivations of the attitude solution from a single vector observation along with its variance analysis are presented. The proposed filter is named as the Single Vector Observation Linear Kalman filter (SVO-LKF). Flexible design of the filter facilitates fast execution speed with respect to other filters with linearization. Simulations and experiments are conducted in the presence of large external acceleration and magnetic distortion. The results show that, compared with representative filtering methods and attitude observers, the SVO-LKF owns the best estimation accuracy and it consumes much less time in the fusion process.

Weighted Kalman Filter Phase Unwrapping Algorithm Based on the Phase Derivative Variance Map

2013 ◽  
Vol 475-476 ◽  
pp. 991-995
Author(s):  
Li Fen Wang ◽  
Man Yan
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
State Space Model ◽  
Phase Unwrapping ◽  
Noise Elimination ◽  
Data Application ◽  
Vector Observation ◽  
Phase Derivative ◽  
Steep Terrain ◽  
Error Transfer

The kalman filtering phase unwrapping is a state estimation problem. It can realize phase unwrapping and noise elimination at the same time, and calculate the real phase by establishing the state space model and vector observation model. In the steep terrain, the conventional kalman filtering algorithm unwrapping results are often not accurate, easy to cause the error transfer. Aiming at this problem, the weighted kalman filter phase unwrapping algorithm based on the phase derivative variance map is proposed. The values of the phase derivative variance maps are applied to determine the noise variance in the observation equation, then the weighted kalman filter is used to unwrap phase, this can increase the accuracy of the results. Finally, experiments are carried out in the InSAR data application under the condition of steep terrain, and with the conventional kalman filtering phase unwrapping algorithm are compared, the effectiveness of the proposed method is verified.

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