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A Lightweight Cubature Kalman Filter for Attitude and Heading Reference Systems Using Simplified Prediction Equations

Shunsei Yamagishi, Lei Jing

TL;DR

The controlled experimental results show that the KCKF reduces the computation time by approximately 19% compared to the CKF on a high-performance computer, whereas the KCKF reduces the computation time by approximately 15% compared to the CKF on a low-cost single-board computer.

Abstract

Attitude and Heading Reference Systems (AHRSs) are broadly applied wherever reliable orientation and motion sensing is required. In this paper, we present an improved Cubature Kalman Filter (CKF) with lower computational cost while maintaining estimation accuracy, which is named "Kaisoku Cubature Kalman Filter (KCKF)". The computationally efficient equations of the KCKF are derived by simplifying those of the CKF, while preserving equivalent mathematical relations. The lightweight prediction equations in the KCKF are derived by expanding the summation terms in the CKF and simplifying the result. This paper shows that the KCKF requires fewer floating-point operations (FLOPs) than the CKF. The controlled experimental results show that the KCKF reduces the computation time by approximately 19% compared to the CKF on a high-performance computer, whereas the KCKF reduces the computation time by approximately 15% compared to the CKF on a low-cost single-board computer. In addition, the KCKF maintains the attitude estimation accuracy of the CKF.

A Lightweight Cubature Kalman Filter for Attitude and Heading Reference Systems Using Simplified Prediction Equations

TL;DR

The controlled experimental results show that the KCKF reduces the computation time by approximately 19% compared to the CKF on a high-performance computer, whereas the KCKF reduces the computation time by approximately 15% compared to the CKF on a low-cost single-board computer.

Abstract

Attitude and Heading Reference Systems (AHRSs) are broadly applied wherever reliable orientation and motion sensing is required. In this paper, we present an improved Cubature Kalman Filter (CKF) with lower computational cost while maintaining estimation accuracy, which is named "Kaisoku Cubature Kalman Filter (KCKF)". The computationally efficient equations of the KCKF are derived by simplifying those of the CKF, while preserving equivalent mathematical relations. The lightweight prediction equations in the KCKF are derived by expanding the summation terms in the CKF and simplifying the result. This paper shows that the KCKF requires fewer floating-point operations (FLOPs) than the CKF. The controlled experimental results show that the KCKF reduces the computation time by approximately 19% compared to the CKF on a high-performance computer, whereas the KCKF reduces the computation time by approximately 15% compared to the CKF on a low-cost single-board computer. In addition, the KCKF maintains the attitude estimation accuracy of the CKF.
Paper Structure (11 sections, 23 equations, 14 figures, 5 tables, 1 algorithm)

This paper contains 11 sections, 23 equations, 14 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Methods
  3. State Model and Observation Model of Attitude Estimation Algorithm
  4. Kaisoku Cubature Kalman Filter and Cubature Kalman Filter
  5. Derivation of the Prediction Formulas of the KCKF
  6. Comparing the Computational Cost of KCKF With That of CKF
  7. Experiments
  8. Exp.1 Evaluating the Errors of the Kalman Filters
  9. Exp.2 Comparing the Computation Time of the Kalman Filters
  10. Conclusion
  11. Proof

Figures (14)

  • Figure 1: The algorithm of the CKF
  • Figure 2: The algorithm of the KCKF
  • Figure 3: MTW2-3A7G6 mounted on a shoe
  • Figure 4: The estimation results of attitudes by each attitude estimator in Data 2-1
  • Figure 5: The estimation results of attitudes by each attitude estimator in Data 3-1 from 100 seconds to 130 seconds
  • ...and 9 more figures