An ML estimation based robust Chinese remainder theorem for reals

Wenjie Wang, Xiaoping Li, Xiang Gen Xia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum likelihood (ML) estimation based CRT when the remainder noises may not necessarily have the same variances. The proposed algorithm only needs to search for the solution among L elements, where L is the number of remainders. We compare the performances of the newly proposed algorithm and the existing algorithm in term of numerical simulations. The results demonstrate that the proposed algorithm not only has a better performance when the remainders have different error levels/variances, but also has a much lower computational complexity.

Original languageEnglish
Title of host publication2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages363-367
Number of pages5
ISBN (Electronic)9781479919482
DOIs
Publication statusPublished - 31 Aug 2015
Externally publishedYes
EventIEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Chengdu, China
Duration: 12 Jul 201515 Jul 2015

Publication series

Name2015 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015 - Proceedings

Conference

ConferenceIEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2015
Country/TerritoryChina
CityChengdu
Period12/07/1515/07/15

Keywords

  • Chinese remainder theorem (CRT)
  • phase unwrapping
  • residue number system
  • robustness

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