Location and continuous orientation estimation of buried targets using tensor extraction

2013 ◽  
Author(s):  
Kyle Krueger ◽  
Waymond R. Scott ◽  
James H. McClellan
Keyword(s):  
Orientation Estimation ◽  
Continuous Orientation

scholarly journals The Orientation Estimation of Elongated Underground Objects via Multipolarization Aggregation and Selection Neural Network

2021 ◽  
pp. 1-5
Author(s):  
Hai-Han Sun ◽  
Yee Hui Lee ◽  
Chongyi Li ◽  
Lai Fern Ow ◽  
Mohamed Lokman Mohd Yusof ◽  
...  
Keyword(s):  
Neural Network ◽  
Orientation Estimation

scholarly journals Novel MARG-Sensor Orientation Estimation Algorithm Using Fast Kalman Filter

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Siwen Guo ◽  
Jin Wu ◽  
Zuocai Wang ◽  
Jide Qian
Keyword(s):  
Kalman Filter ◽  
Process Model ◽  
Attitude Determination ◽  
Angular Rate ◽  
Estimation Algorithm ◽  
Measurement Model ◽  
Estimation Accuracy ◽  
Orientation Estimation ◽  
Kinematic Equation ◽  
Sensor Orientation

Orientation estimation from magnetic, angular rate, and gravity (MARG) sensor array is a key problem in mechatronic-related applications. This paper proposes a new method in which a quaternion-based Kalman filter scheme is designed. The quaternion kinematic equation is employed as the process model. With our previous contributions, we establish the measurement model of attitude quaternion from accelerometer and magnetometer, which is later proved to be the fastest (computationally) one among representative attitude determination algorithms of such sensor combination. Variance analysis is later given enabling the optimal updating of the proposed filter. The algorithm is implemented on real-world hardware where experiments are carried out to reveal the advantages of the proposed method with respect to conventional ones. The proposed approach is also validated on an unmanned aerial vehicle during a real flight. Results show that the proposed one is faster than any other Kalman-based ones and even faster than some complementary ones while the attitude estimation accuracy is maintained.

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