Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization

Parodi, Bruno Betoni; Lenz, H.; Szabo, Andrei; Wang, Hui; Horn, Joachim; Bamberger, Joachim; Obradović, Dragan

doi:10.1109/plans.2006.1650600

Cited by 21 publications

(7 citation statements)

References 21 publications

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“…During the E-step, the user traces are modeled by Hidden Markov Models (HMM), whereas the M-step adjusts the HMM parameters, including the radio map, to the determined track. Parodi et al apply unsupervised Self-Organizing Map (SOM) learning in order to improve the quality of a RSS RM that is initially filled by a propagation model [10], [11]. As a continuation of this work, this paper extends and completes this approach towards propagation time based systems.…”

Section: Related Workmentioning

confidence: 88%

“…Similar to [5], [10], our RM R stores the measurement values for a set of K two-dimensional positions:…”

Section: B Radio Map and Pattern Matching Conceptmentioning

confidence: 99%

“…The idea of using Kohonen's self organizing map for unsupervised learning of location fingerprints was presented in [10] for the first time. Results show that the accuracy reached by unsupervised learning systems is similar to calibrated fingerprints.…”

Section: A Som Learningmentioning

confidence: 99%

“…Results show that the accuracy reached by unsupervised learning systems is similar to calibrated fingerprints. However, methods presented in [10] are sensitive to parameters which are time dependent and tuned separately for each test case.…”

Section: A Som Learningmentioning

confidence: 99%

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Unsupervised Learning of Propagation Time for Indoor Localization

Szabo

Weiherer

Bamberger

2011

2011 IEEE 73rd Vehicular Technology Conference (VTC Spring)

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Many of today's localization systems for indoor and outdoor positioning are based on propagation time measurements of radio signals. In order to achieve high positioning accuracy in presence of non line of sight (NLOS) propagation, these systems require either an expensive manual calibration or additional information. In this paper, we present a novel approach for a channel impulse response (CIR) based fingerprint system, which reduces the calibration and measurement effort and simultaneously improves localization results. The basic idea is to initialize a simple model, which is improved by an online learning procedure using unlabeled measurements. This unsupervised learning algorithm is composed of two independent components, which exploit the similarity of neighboring CIRs as well as the temporal relation of measurements.Our tests indicate a significant improvement compared to traditional methods in case of time difference of arrival (TDoA) measurements. The algorithm can straightforwardly be adapted to arbitrary propagation time measurements.

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Section: Related Workmentioning

confidence: 88%

“…Similar to [5], [10], our RM R stores the measurement values for a set of K two-dimensional positions:…”

Section: B Radio Map and Pattern Matching Conceptmentioning

confidence: 99%

Section: A Som Learningmentioning

confidence: 99%

Section: A Som Learningmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Learning of Propagation Time for Indoor Localization

Szabo

Weiherer

Bamberger

2011

2011 IEEE 73rd Vehicular Technology Conference (VTC Spring)

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“…In the following, we briefly describe the principle and the functionality of each layer. More details can be found in the literature [5,6,23,24,25].…”

Section: Principle Of Wlan Localization Systemsmentioning

confidence: 99%

Enhancing the Map Usage for Indoor Location-Aware Systems

Wang

Lenz

Szabo

et al.

Lecture Notes in Computer Science

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Abstract. Location-aware systems are receiving more and more interest in both academia and industry due to their promising prospective in a broad category of so-called Location-Based-Services (LBS). The map interface plays a crucial role in the location-aware systems, especially for indoor scenarios. This paper addresses the usage of map information in a Wireless LAN (WLAN)-based indoor navigation system. We describe the benefit of using map information in multiple algorithms of the system, including radio-map generation, tracking, semantic positioning and navigation. Then we discuss how to represent or model the indoor map to fulfill the requirements of intelligent algorithms. We believe that a vector-based multi-layer representation is the best choice for indoor location-aware system.

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Overview of Indoor Navigation Techniques

Pasricha

2020

Position, Navigation, and Timing Technologies in the 21st Century

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Initialization and Online-Learning of RSS Maps for Indoor / Campus Localization

Cited by 21 publications

References 21 publications

Unsupervised Learning of Propagation Time for Indoor Localization

Unsupervised Learning of Propagation Time for Indoor Localization

Enhancing the Map Usage for Indoor Location-Aware Systems

Overview of Indoor Navigation Techniques

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