Design, implementation &amp;#x00026; testing of positioning techniques in mobile networks

Lakmali, B.D.S.; Wijesinghe, W.H.M.P; Silva, K.U.M. De; Liyanagama, K.G.; Dias, S.A.D.

doi:10.1109/iciafs.2007.4544786

Cited by 15 publications

(6 citation statements)

References 6 publications

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“…Based on a similarity measure such as a distance function, the KNN algorithm determines the k closest matches in the signal space to the target. Then, the location of the target can be estimated by the average of the coordinates of the k neighbors [3]. Generative localization methods apply statistical approaches, e.g., Hidden Markov Model [4], Bayesian Inference [5], Gaussian Processes [6], on the Wi-Fi fingerprint database.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Real-Time Indoor Landmark Localization

Zhao

Carrera

Niklaus

et al. 2018

Lecture Notes in Computer Science

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Nowadays, smartphones can collect huge amounts of data from their surroundings with the help of highly accurate sensors. Since the combination of the Received Signal Strengths of surrounding access points and sensor data is assumed to be unique in some locations, it is possible to use this information to accurately predict smartphones' indoor locations. In this work, we apply machine learning methods to derive the correlation between smartphones' locations and the received Wi-Fi signal strength and sensor values. We have developed an Android application that is able to distinguish between rooms on a floor, and special landmarks within the detected room. Our real-world experiment results show that the Voting ensemble predictor outperforms individual machine learning algorithms and it achieves the best indoor landmark localization accuracy of 94% in office-like environments. This work provides a coarse-grained indoor room recognition and landmark localization within rooms, which can be envisioned as a basis for accurate indoor positioning.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Real-Time Indoor Landmark Localization

Zhao

Carrera

Niklaus

et al. 2018

Lecture Notes in Computer Science

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“…Based on a similarity measure such as a distance function, the KNN algorithm determines the k closest matches in the signal space to the target. Then, the location of the target can be estimated as the average of the coordinates of the k neighbors [25]. Generative localization methods apply statistical approaches, e.g., Hidden Markov Model [28], Bayesian Inference [31], Gaussian Processes [29], on the fingerprint database.…”

Section: Related Workmentioning

confidence: 99%

Discriminative Learning-based Smartphone Indoor Localization

Carrera,

Zhao,

Braun

et al. 2018

Preprint

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Due to the growing area of ubiquitous mobile applications, indoor localization of smartphones has become an interesting research topic. Most of the current indoor localization systems rely on intensive site survey to achieve high accuracy. In this work, we propose an efficient smartphones indoor localization system that is able to reduce the site survey effort while still achieving high localization accuracy. Our system is built by fusing a variety of signals, such as Wi-Fi received signal strength indicator, magnetic field and floor plan information in an enhanced particle filter. To achieve high and stable performance, we first apply discriminative learning models to integrate Wi-Fi and magnetic field readings to achieve room level landmark detection. Further, we integrate landmark detection, range-based localization models, with a graph-based discretized system state representation. Because our approach requires only discriminative learning-based room level landmark detections, the time spent in the learning phase is significantly reduced compared to traditional Wi-Fi fingerprinting or landmark-based approaches. We conduct experimental studies to evaluate our system in an office-like indoor environment. Experiment results show that our system can significantly reduce the learning efforts, and the localization method can achieve performance with an average localization error of 1.55 meters.

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“…Similar with KNN, but using weighted averaging the locations of selected k number of NNs in KWNN(k weighted nearest neighbours) [14]. Usually, the KNN and KWNN can obtain a better accuracy when compared with the NN method.…”

Section: B Fingerprintingmentioning

confidence: 99%

An improved fingerprinting method for localization WLAN-based

Shang

Zheng

Zhu

2011

2011 International Conference on Computer Science and Service System (CSSS)

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Many location-based services require location awareness, but it is often too expensive to include a GPS receiver in every network node. Hence, localization schemes using radio signal strength is more attractive when high precision isn't in demand. Fingerprinting is an effective method, but it is inaccurate when accidental gross error is included in the measurement. In this paper, we exploit an improved fingerprinting technique to overcome the accidental gross error caused by mobile person or other interference sources. By setting a threshold of signal distance, these accidental errors can be eliminated. Experimental results indicate that the improved method can locate a client with an average accuracy of 1-2m.What's more, it is more robust than basic fingerprinting. 2051978-1-4244-9763-8/11/$26.00 ©2011 IEEE

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Design, implementation & testing of positioning techniques in mobile networks

Cited by 15 publications

References 6 publications

Machine Learning-Based Real-Time Indoor Landmark Localization

Machine Learning-Based Real-Time Indoor Landmark Localization

Discriminative Learning-based Smartphone Indoor Localization

An improved fingerprinting method for localization WLAN-based

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