Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning

Hoàng, Mạnh Kha; Haeb‐Umbach, Reinhold

doi:10.1109/icassp.2013.6638353

Cited by 17 publications

(21 citation statements)

References 7 publications

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“…Standard EM algorithm for GMM [5] After [3] Proposed EM algorithm for C-GMM As is evident, when c = µ 1 -2σ 1 = -96, data nearly do not suffer from censoring (almost complete); the proposal and the standard EM algorithm for GMM produced the same results. However, when c changes from -93 to -84, the proposed EM algorithm introduces improved results.…”

Section: (Dbm)mentioning

confidence: 92%

“…Positioning results were improved relative to the single Gaussian model. However, the censoring problem has not been considered in these studies, although they clearly occurred, as discussed in [3,4].…”

Section: Faculty Of Electronics Hanoi University Of Industry 2 Natiomentioning

confidence: 99%

“…Moreover, if the data are complete data ( ), then these equations are reduced to the standard EM algorithm for the mixture Gaussian data [5]. On the other hand, if the data have a single Gaussian distribution and suffered from the censoring problem, by setting over three formulae become those reported in [3]. This means that the proposal can handle both the censoring and multi-component problems (9) Given equations (6÷8), both observable and censored mixture data contribute to the estimates.…”

Section: M-stepmentioning

confidence: 99%

“…The probability density function (PDF) of the observed data is assumed to be the single Gaussian in the presence of censoring and dropping problems [3,4]. Censoring occurs due to the limited sensitivity of Wi-Fi sensors or the sensor driver, which does not intentionally report the overly weak observed signal strengths; in other words, the smart phones do not report the signal strength if it is below a specific threshold, e.g., -100 dBm with typical smart phones.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Vu¹,

Le²

2019

VJSTE

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In complex indoor environments, due to the attenuation of the signal and the changing surrounding environment, the censoring and multi-component problems may be present in the observed data. Censoring refers to the fact that sensors on portable devices cannot measure Received Signal Strength Index (RSSI) values below a specific threshold, such as -100 dBm. The multi-component problem occurs when the measured data varies due to obstacles and user directions, whether the door is closed or open, etc. By accounting for these problems, this paper proposes to model the RSSI probability density distributions using the Censoring Gaussian Mixture Model (C-GMM) and develop the Expectation-Maximization (EM) algorithm to estimate the parameters of this model in the offline phase of the Wi-Fi fingerprinting based Indoor Positioning Systems (IPS). The simulation results demonstrate the effectiveness of the proposed method.

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Section: (Dbm)mentioning

confidence: 92%

Section: Faculty Of Electronics Hanoi University Of Industry 2 Natiomentioning

confidence: 99%

Section: M-stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Vu¹,

Le²

2019

VJSTE

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“…Besides evaluating SEMcm algorithm on some trivial data sets (one component with left-censoring [11]; one doublycensored component), we successfully evaluated SEMcmG on a simulated mixture of two left-censored components, which was meant to emulate interference affected RSSI samples. The first component represents the signal over a distance range identical to the range considered in the empirical data evaluation: l d = 23−−32, where l d is the log-distance, defined as 10 log 10 (distance in m).…”

Section: A Model Evaluation On Simulated Datamentioning

confidence: 99%

EM-based channel estimation from crowd-sourced RSSI samples corrupted by noise and interference

Kokalj-Filipović

Greenstein

2015

2015 49th Annual Conference on Information Sciences and Systems (CISS)

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We propose a method for estimating channel parameters from RSSI measurements and the lost packet count, which can work in the presence of losses due to both interference and signal attenuation below the noise floor. This is especially important in the wireless networks, such as vehicular, where propagation model changes with the density of nodes. The method is based on Stochastic Expectation Maximization, where the received data is modeled as a mixture of distributions (no/low interference and strong interference), incomplete (censored) due to packet losses. The PDFs in the mixture are Gamma, according to the commonly accepted model for wireless signal and interference power. This approach leverages the loss count as additional information, hence outperforming maximum likelihood estimation, which does not use this information (ML-), for a small number of received RSSI samples. Hence, it allows inexpensive on-line channel estimation from ad-hoc collected data. The method also outperforms ML-on uncensored data mixtures, as ML-assumes that samples are from a single-mode PDF. †

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

Hoàng

2019

ICT Express

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Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning

Cited by 17 publications

References 7 publications

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

Gaussian Mixture Modeling for Wi-Fi fingerprintingbased indoor positioning in the presence of censored data

EM-based channel estimation from crowd-sourced RSSI samples corrupted by noise and interference

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

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