1P2-V08 Attitude Estimation of a Space Floating Object with Unknown Disturbance Using Particle Filter

A Rao-Blackwellized particle filter has been designed and its performance investigated in a simulated three-axis satellite testbed used for evaluating on-board attitude estimation and control algorithms. Vector measurements have been used to estimate attitude and angular rate and, additionally, a pseudo-measurement based on a low-pass filtered time-derivative of the vector measurements has been proposed to improve the filter performance. Conventional extended and unscented Kalman filters, and standard particle filtering have been compared with the proposed approach to gauge its performance regarding attitude and angular rate estimation accuracy, computational workload, convergence rate under uncertain initial conditions, and sensitivity to disturbances. Though a myriad of filters have been proposed in the past to tackle the problem of spacecraft attitude and angular rate estimation with vector observations, to the best knowledge of the authors the present Rao-Blackwellized particle filter is a novel approach that significantly reduces the computational load, provides an attractive convergence rate, and successfully preserves the performance of the standard particle filter when subjected to disturbances.

Download Full-text

A Discrete Quaternion Particle Filter Based on Deterministic Sampling for IMU Attitude Estimation

IEEE Sensors Journal ◽

10.1109/jsen.2021.3109156 ◽

2021 ◽

pp. 1-1

Author(s):

Zhaihe Zhou ◽

Chuanwei Zeng ◽

Xiangrui Tian ◽

Qingxi Zeng ◽

Rui Yao

Keyword(s):

Particle Filter ◽

Attitude Estimation ◽

Deterministic Sampling

Download Full-text

Experimental validation of attitude estimation of an orbital object with minimum mean square error approach and particle filter

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2016.2a2-17a6 ◽

2016 ◽

Vol 2016 (0) ◽

pp. 2A2-17a6

Author(s):

Satoshi UEDA ◽

Satoko ABIKO

Keyword(s):

Particle Filter ◽

Mean Square Error ◽

Experimental Validation ◽

Minimum Mean Square Error ◽

Attitude Estimation ◽

Mean Square

Download Full-text

Spacecraft attitude estimation from vector measurements using particle filter

2005 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2005.1527031 ◽

2005 ◽

Cited By ~ 1

Author(s):

Xue-Yuan Jiang ◽

Guang-Fu Ma

Keyword(s):

Particle Filter ◽

Attitude Estimation ◽

Spacecraft Attitude

Download Full-text

Pipeline Gaussian Particle Filter and Hardware Design for Attitude Estimation with UAV

Mathematical Problems in Engineering ◽

10.1155/2020/7092526 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Yali Xue ◽

Hu Chen ◽

Jie Chen ◽

Jiahui Wang

Keyword(s):

Particle Filter ◽

Real Time ◽

Gaussian Noise ◽

Estimation Algorithm ◽

Attitude Estimation ◽

Estimation Accuracy ◽

Gaussian State ◽

Time Performance ◽

Simulation Results ◽

Non Gaussian

This paper based on the Gaussian particle filter (GPF) deals with the attitude estimation of UAV. GPF algorithm has better estimation accuracy than the general nonlinear non-Gaussian state estimation and is usually used to improve the system’s real-time performance whose noise is specific such as Gaussian noise during the mini UAV positioning and navigation. The attitude estimation algorithm is implemented on FPGA to verify the effectiveness of the Gaussian particle filter. Simulation results have illustrated that the GPF algorithm is effective and has better real-time performance than that of the particle filter.

Download Full-text