EMD-based detection, identification and adaptation of cycle-slip for GNSS relative positioning

Shiou Gwo Lin, Shih Yin Chen, Shuen De Wu, Ou Yang Mang, Feng Chi Yu*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cycle-slip causes discontinuous integer cycle jumps in phase measurements, detecting and correcting cycle-slip is an important issue for GNSS relative positioning. In this paper, a novel method is proposed to deal with the problem based on empirical mode decomposition (EMD) method. The proposed method consists of three steps. Firstly, a time differentiated GNSS phase signal is decomposed into a trend signal and a finite number of intrinsic mode functions (IMFs) by EMD. Secondly, a "cycle-slip signal" is obtained by removing the trend signal and low frequency IMFs from the original signal. Finally, an edge detection operator is applied on the "cycle-slip signal" to detect and repair the cycle-slip. Experiments verified the success rate of the algorithm up to 100 % (15° mask angle) in various simulated scenarios. Several tests were performed based on real data over multiple days, and the results confirm that the proposed algorithm is applicable for relative positioning.

Original languageEnglish
Title of host publication33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Pages2145-2150
Number of pages6
Publication statusPublished - 2012
Event33rd Asian Conference on Remote Sensing 2012, ACRS 2012 - Pattaya, Thailand
Duration: 2012 Nov 262012 Nov 30

Publication series

Name33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Volume3

Other

Other33rd Asian Conference on Remote Sensing 2012, ACRS 2012
Country/TerritoryThailand
CityPattaya
Period2012/11/262012/11/30

Keywords

  • Cycle-slip
  • EMD
  • GNSS

ASJC Scopus subject areas

  • Information Systems

Fingerprint

Dive into the research topics of 'EMD-based detection, identification and adaptation of cycle-slip for GNSS relative positioning'. Together they form a unique fingerprint.

Cite this