Orientation estimation using vector observations with one or two components lost

Abstract

Purpose

Traditional algorithms require at least two complete vector observations to estimate orientation parameters. However, sensor faults and disturbances may cause some components of vector observations unavailable. This paper aims to propose algorithms to realize orientation estimation using vector observations with one or two components lost.

Design/methodology/approach

The fundamental of the proposed method is using norm equation and dot product equation to estimate the lost components, then, using an improved TRIAD to calculate attitude matrix. Specific algorithms for one and two lost components cases are constructed respectively, and the nonuniqueness of orientation estimation is analyzed from a geometric point of view. At last, experiments are performed to test the proposed algorithms.

Findings

The loss of components results in the loss of orientation information. The introduction of the norm equation and dot product equation can partially compensate for the loss of information. Experiment results and analysis show that the proposed algorithms can provide effective orientation estimation, and in vast majority of applications, the proposed algorithms can provide a unique solution in one lost component case and double solutions in two lost components case.

Originality/value

The proposed method addresses the problem of orientation estimation when one or two components of vector observations are unavailable. The introduction of the norm equation and dot product equation makes the calculation cost low, while the analyses from a geometric point of view makes the study of nonuniqueness more intuitive.