An EM algorithm for GMM parameter estimation in the presence of censored and dropped data with potential application for indoor positioning | Zendy

Trung Kien Vu | Zendy; Mạnh Kha Hoàng | Zendy; Hung Lan Le | Zendy

Open Access

An EM algorithm for GMM parameter estimation in the presence of censored and dropped data with potential application for indoor positioning

Author(s) -

Trung Kien Vu,

Mạnh Kha Hoàng,

Hung Lan Le

Publication year - 2018

Publication title -

ict express

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.733

H-Index - 22

ISSN - 2405-9595

DOI - 10.1016/j.icte.2018.08.001

Subject(s) - mixture model , expectation–maximization algorithm , algorithm , gaussian , maximum likelihood , computer science , missing data , statistics , mathematics , physics , quantum mechanics

In this paper, a specific type of incomplete data in Wi-Fi fingerprinting based indoor positioning systems (WF-IPS) is presented: censored and dropped mixture data. For fitting this type of data, a censored and dropped Gaussian Mixture Model (CD-GMM) was proposed. Further, an extended version of the Expectation–Maximization (EM) algorithm is developed for estimating parameters of this model. Simulation results show the advantage of our proposal compared to existing methods. Thus, this approach not only has potential for the WF-IPSs, but also for other applications.

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