Application and Realization of Indoor Localization Based on Hidden Markov Model

2014 ◽  
pp. 303-312
Author(s):  
Xinlang Ding ◽  
Yubin Chen ◽  
Qiao Gui ◽  
Chong Xiong
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

scholarly journals WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6778
Author(s):  
Oluwaseyi Paul Babalola ◽  
Vipin Balyan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Localization Performance

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

A Hidden Markov Model based smartphone heterogeneity resilient portable indoor localization framework

2020 ◽  
Vol 108 ◽  
pp. 101806
Author(s):  
Saideep Tiku ◽  
Sudeep Pasricha ◽  
Branislav Notaros ◽  
Qi Han
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Model Based

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

Engaging Voluntary Contributions in Online Communities: A Hidden Markov Model

MIS Quarterly ◽  
2018 ◽  
Vol 42 (1) ◽  
pp. 83-100 ◽  
Author(s):  
Wei Chen ◽  
◽  
Xiahua Wei ◽  
Kevin Xiaoguo Zhu ◽  
◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Online Communities ◽  
Hidden Markov ◽  
Voluntary Contributions
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