Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing

Chien Hao Tseng, Sheng-Fuu Lin, Dah Jing Jwo

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

A robust state estimation technique based on the Huber-based Cubature Kalman Filter (HCKF) is proposed for Global Positioning System (GPS) navigation processing. The Cubature Kalman Filter (CKF) employs a third-degree spherical-radial cubature rule to compute the Gaussian weighted integration, such that the numerical instability induced by round-off errors can be avoided. In GPS navigation, the filter-based estimation of the position and velocity states can be severely degraded due to contaminated measurements caused by outliers or deviation from a Gaussian distribution assumption. For the signals contaminated with non-Gaussian noise or outliers, a robust scheme combining the Huber M-estimation methodology and the CKF framework is beneficial where the Huber M-estimation methodology is used to reformulate the measurement information of the CKF. GPS navigation processing using the HCKF algorithm has been carried out and the performance has been compared to those based on the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and CKF approaches. Simulation and experimental results presented in this paper confirm the effectiveness of the method.

Original languageEnglish
Pages (from-to)527-546
Number of pages20
JournalJournal of Navigation
Volume70
Issue number3
DOIs
StatePublished - 1 May 2017

Keywords

  • Cubature Kalman filter
  • GPS navigation
  • Huber M-estimation
  • Unscented Kalman filter

Fingerprint Dive into the research topics of 'Robust Huber-Based Cubature Kalman Filter for GPS Navigation Processing'. Together they form a unique fingerprint.

  • Cite this