scholarly journals Computationally Efficient Attitude Estimation with Extended H2 Filtering

2020 ◽  
pp. 1-10
Author(s):  
Sunsoo Kim ◽  
Vaishnav Tadiparthi ◽  
Raktim Bhattacharya
Keyword(s):  
Attitude Estimation ◽  
Computationally Efficient

scholarly journals Three-Axis Attitude Estimation Of Satellite Through Only Two-Axis Magnetometer Observations Using LKF Algorithm

2015 ◽  
Vol 22 (4) ◽  
pp. 577-590 ◽  
Author(s):  
Mohamad Fakhari Mehrjardi ◽  
Hilmi Sanusi ◽  
Mohd. Alauddin Mohd. Ali
Keyword(s):  
Kalman Filter ◽  
Space Missions ◽  
Attitude Estimation ◽  
Estimation Problem ◽  
Sensor Data ◽  
Computationally Efficient ◽  
Satellite Attitude ◽  
Simulation Results

Abstract Estimation of satellite three-axis attitude using only one sensor data presents an interesting estimation problem. A flexible and mathematically effective filter for solving the satellite three-axis attitude estimation problem using two-axis magnetometer would be a challenging option for space missions which are suffering from other attitude sensors failure. Mostly, magnetometers are employed with other attitude sensors to resolve attitude estimation. However, by designing a computationally efficient discrete Kalman filter, full attitude estimation can profit by only two-axis magnetometer observations. The method suggested solves the problem of satellite attitude estimation using linear Kalman filter (LKF). Firstly, all models are generated and then the designed scenario is developed and evaluated with simulation results. The filter can achieve 10e-3 degree attitude accuracy or better on all three axes.

scholarly journals An Efficient Nonlinear Filter for Spacecraft Attitude Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Bing Liu ◽  
Zhen Chen ◽  
Xiangdong Liu ◽  
Fan Yang
Keyword(s):  
Estimation Error ◽  
Attitude Estimation ◽  
Local Error ◽  
Computationally Efficient ◽  
Critical Problem ◽  
Estimation Strategy ◽  
The Matrix ◽  
Constant Coefficients ◽  
Error System ◽  
Normalization Constraint

Increasing the computational efficiency of attitude estimation is a critical problem related to modern spacecraft, especially for those with limited computing resources. In this paper, a computationally efficient nonlinear attitude estimation strategy based on the vector observations is proposed. The Rodrigues parameter is chosen as the local error attitude parameter, to maintain the normalization constraint for the quaternion in the global estimator. The proposed attitude estimator is performed in four stages. First, the local attitude estimation error system is described by a polytopic linear model. Then the local error attitude estimator is designed with constant coefficients based on the robustH2filtering algorithm. Subsequently, the attitude predictions and the local error attitude estimations are calculated by a gyro based model and the local error attitude estimator. Finally, the attitude estimations are updated by the predicted attitude with the local error attitude estimations. Since the local error attitude estimator is with constant coefficients, it does not need to calculate the matrix inversion for the filter gain matrix or update the Jacobian matrixes online to obtain the local error attitude estimations. As a result, the computational complexity of the proposed attitude estimator reduces significantly. Simulation results demonstrate the efficiency of the proposed attitude estimation strategy.

Computationally Efficient Adaptive Error-State Kalman Filter for Attitude Estimation

2018 ◽  
Vol 18 (22) ◽  
pp. 9332-9342 ◽  
Author(s):  
Michael B. Del Rosario ◽  
Heba Khamis ◽  
Phillip Ngo ◽  
Nigel H. Lovell ◽  
Stephen J. Redmond
Keyword(s):  
Kalman Filter ◽  
Attitude Estimation ◽  
Computationally Efficient ◽  
Error State

Computationally efficient model of the implanted knee for time-sensitive applications

2020 ◽  
Author(s):  
E Bori ◽  
A Navacchia ◽  
L Wang ◽  
L Duxbury ◽  
S McGuan ◽  
...  
Keyword(s):  
Computationally Efficient

Cloud-Based Multimedia Content Protection System

2020 ◽  
pp. 164-169
Author(s):  
B. Aparna ◽  
S. Madhavi ◽  
G. Mounika ◽  
P. Avinash ◽  
S. Chakravarthi
Keyword(s):  
Large Scale ◽  
Protection System ◽  
Multimedia Content ◽  
Computationally Efficient ◽  
Content Protection ◽  
Protection Systems ◽  
Multimedia Content Protection ◽  
High Scalability ◽  
Cloud Infrastructures ◽  
Rapid Deployment

We propose a new design for large-scale multimedia content protection systems. Our design leverages cloud infrastructures to provide cost efficiency, rapid deployment, scalability, and elasticity to accommodate varying workloads. The proposed system can be used to protect different multimedia content types, including videos, images, audio clips, songs, and music clips. The system can be deployed on private and/or public clouds. Our system has two novel components: (i) method to create signatures of videos, and (ii) distributed matching engine for multimedia objects. The signature method creates robust and representative signatures of videos that capture the depth signals in these videos and it is computationally efficient to compute and compare as well as it requires small storage. The distributed matching engine achieves high scalability and it is designed to support different multimedia objects. We implemented the proposed system and deployed it on two clouds: Amazon cloud and our private cloud. Our experiments with more than 11,000 videos and 1 million images show the high accuracy and scalability of the proposed system. In addition, we compared our system to the protection system used by YouTube and our results show that the YouTube protection system fails to detect most copies of videos, while our system detects more than 98% of them.

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