Attitude estimation based on inertial and position measurements

Attitude estimation arises in many applications, and is crucial for the aerospace and aeronautic industry. A classical approach to perform the attitude estimation is via Inertial Measurement Unit (IMU) data and a set of measured observation vectors, both in the object and inertial reference frame. The attitude estimation can be then formulated as a constrained least-squares problem. As a possible alternative, attitude estimation can be performed using IMU data and a set of measured positions, hence removing the need for observation vectors. Attitude estimation based on IMU and position measurements is already undergoing industrial research. However, the underlying estimation problem does not necessarily admit a unique solution, and can therefore degenerate depending on the object trajectories. This paper theoretically investigates this approach to attitude estimation, and provides a simple, explicit relationship between the standard deviation of the attitude estimation and the object trajectories, which is independent of the choice of parametrization of the special orthogonal group SO(3).

[1]  H. Bock,et al.  A Multiple Shooting Algorithm for Direct Solution of Optimal Control Problems , 1984 .

[2]  Jeroen D. Hol,et al.  Sensor Fusion and Calibration of Inertial Sensors, Vision, Ultra-Wideband and GPS , 2011 .

[3]  Moritz Diehl,et al.  ACADO toolkit—An open‐source framework for automatic control and dynamic optimization , 2011 .

[4]  P. B. Davenport A vector approach to the algebra of rotations with applications , 1968 .

[5]  Daniel Choukroun,et al.  Optimal-REQUEST Algorithm for Attitude Determination , 2001 .

[6]  J. Stuelpnagel,et al.  A Least Squares Estimate of Satellite Attitude (Grace Wahba) , 1966 .

[7]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[8]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .

[9]  M. Shuster,et al.  Three-axis attitude determination from vector observations , 1981 .

[10]  Malcolm D. Shuster,et al.  The generalized Wahba problem , 2006 .

[11]  Johannes P. Schlöder,et al.  Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes , 2004, Optim. Methods Softw..

[12]  G. Wahba A Least Squares Estimate of Satellite Attitude , 1965 .

[13]  M. U. de Haag,et al.  Integrating GPS/MEMS-based-IMU with single GPS baseline for improved heading performance , 2012, 2012 IEEE Aerospace Conference.

[14]  F. Markley Attitude determination using vector observations: A fast optimal matrix algorithm , 1993 .

[15]  F. Landis Markley,et al.  Attitude Estimation or Quaternion Estimation , 2003 .