Survey of Nonlinear Attitude Estimation Methods

Abstract

This paper provides a survey of modern nonlinear filtering methods for attitude estimation. Early applications relied mostly on the extended Kalman filter for attitude estimation. Since these applications, several new approaches have been developed that have proven to be superior to the extended Kalman filter. Several of these approaches maintain the basic structure of the extended Kalman filter, but employ various modifications in order to provide better convergence or improve other performance characteristics. Examples of such approaches include: filter QUEST, extended QUEST and the backwards-smoothing extended Kalman filter. Filters that propagate and update a discrete set of sigma points rather than using linearized equations for the mean and covariance are also reviewed. A twostep approach is discussed with a first-step state that linearizes the measurement model and an iterative second step to recover the desired attitude states. These approaches are all based on the Gaussian assumption that the probability density function is adequately specified by its mean and covariance. Other approaches that do not require this assumption are reviewed,

Nonlinear observer for GNSS-aided inertial navigation with quaternion-based attitude estimation

“…Nonlinear attitude estimation has received significant attention as a stand-alone problem [6]- [13], producing a number of different designs that rely on either an attitude measurement or a set of vector measurements in the body-fixed frame that can be compared with reference vectors in the navigation frame. An extensive survey of attitude estimation methods is given by Crassidis, Markley, and Cheng [14].…”

Observers for interconnected nonlinear and linear systems

“…Most of the effort on nonlinear navigation observers has been directed toward the problem of estimating attitude, typically based on an explicit attitude measurement or a comparison between vectors in two coordinate frames (see Crassidis, Markley, and Cheng, 2007, for a survey). Vik and Fossen (2001) studied GNSS/INS integration, with the assumption that the attitude could be measured independently from the position and velocity.…”

Adaptive Estimation of Measurement Bias in Three-Dimensional Field Sensors with Angular Rate Sensors: Theory and Comparative Experimental Evaluation

“…Currently many integrated devices incorporate angular-rate gyroscopes combined with accelerometers and magnetometers to enable estimation of the full attitude (e.g., heading, pitch and roll) of the instrument. Several methods are available to estimate the attitude with these sensors -see the recent Crassidis et al's review of attitude estimation methods [6]. All of these sensors are affected by biases, scale factors, and non-orthogonality of their measurements.…”