Low-dimensional signal-strength fingerprint-based positioning in wireless LANs

Milioris, Dimitris; Tzagkarakis, George; Papakonstantinou, Artemis; Papadopouli, Maria; Tsakalides, Panagiotis

doi:10.1016/j.adhoc.2011.12.006

Cited by 74 publications

(51 citation statements)

References 28 publications

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“…Moreover, it is difficult to get a suitable signal transmission when indoor environment is complicated and changeable. There are many different indoor positioning methods for wireless network [1], for example, time-based positioning [2], angle-based positioning [3], received signal strength indicator(RSSI) based modeling positioning [4,5,6] and RSSI-based fingerprint positioning [7,8,9,10,11,12,13]. In current indoor positioning systems, the approaches of RSSI-based modeling positioning and RSSI-based fingerprint positioning are relatively mature.…”

Section: Introductionmentioning

confidence: 99%

RSSI-Based Fingerprint Positioning System for Indoor Wireless Network

Yang

Zhang

2014

Communications in Computer and Information Science

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Abstract. This paper presents a direct explicit method of the fingerprint positioning for indoor wireless network. In data collection, for the purpose of a reliable and stable signal, a feedback filter is added to the sampler. In positioning phase, the location clustering technique is used to exclude invalid reference points. Then a matching algorithm based on RSSI correlation coefficient is proposed, which can improve positioning accuracy. The example in the paper illustrates the effectiveness of the proposed positioning scheme.

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Section: Introductionmentioning

confidence: 99%

RSSI-Based Fingerprint Positioning System for Indoor Wireless Network

Yang

Zhang

2014

Communications in Computer and Information Science

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“…Let us first describe the main theoretical concepts of CS [9] as applied in the context of classification. Let x ∈ R N denote the signal of interest.…”

Section: Compressive Sensingmentioning

confidence: 99%

Topic detection and compressed classification in Twitter

Milioris

Jacquet

2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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In this paper we introduce a novel information propagation method in Twitter, while maintaining a low computational complexity. It exploits the power of Compressive Sensing in conjunction with a Kalman filter to update the states of a dynamical system. The proposed method first employs Joint Complexity, which is defined as the cardinality of a set of all distinct factors of a given string represented by suffix trees, to perform topic detection. Then based on the inherent spatial sparsity of the data, we apply the theory of Compressive Sensing to perform sparsity-based topic classification by recovering an indicator vector, while reducing significantly the amount of information from tweets, possessing limited power, storage, and processing capabilities, to a central server. We exploit datasets in various languages collected by using the Twitter streaming API and achieve better classification accuracy when compared with state-of-the-art methods.

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“…In a conventional grid-cell layout (CGCL) based RF fingerprinting method using Kullback-Leibler Divergence (KLD) has two main phases [3], [10], [11]: Training Phase: First an offline processing and manipulation of MDT correlation database takes place. A layout of adjoining rectangular or square grid-cell units are formed over the whole geographical area of interest.…”

Section: B Grid-cell Based Rf Fingerprintingmentioning

confidence: 99%

“…Receivers in Global Navigation Satellite Systems (GNSS) such as GPS or GLONASS tend to output inaccurate location estimations while operating in urban regions, mostly due to the density of tall buildings, which often block a receiver's line of sight to the navigation satellites [2]. Among the non-standard positioning methods included in LTE Release 9, RF fingerprinting is the most cost-efficient solution for indoor WLAN positioning [3], [4], [5] as well as for outdoor mobile cellular positioning in densely built urban environments [6], [7]. RF fingerprinting, also known as database correlation method (DCM) finds a user's position by mapping RF measurements obtained from the UE onto an RF map, where the map is typically based on detailed RF predictions or site surveying results.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks

Mondal

Ristaniemi

Turkka

2015

2015 International Conference on Location and GNSS (ICL-GNSS)

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Abstract-In this paper we propose a novel optimization algorithm for grid-based RF fingerprinting to improve user equipment (UE) positioning accuracy. For this purpose we have used Multi-objective Genetic Algorithm (MOGA) which enables autonomous calibration of gridcell layout (GCL) for better UE positioning as compared to that of the conventional fingerprinting approach. Performance evaluations were carried out using two different training data-sets consisting of Minimization of Drive Testing measurements obtained from a dynamic system simulation in a heterogeneous LTE small cell environment. The robustness of the proposed method has been tested analyzing positioning results from two different areas of interest. Optimization of GCL is performed in two ways: (1) array-wise calibration of the grid-cell units using non-overlapping GCL and (2) creating an overlapping GCL to cover of whole simulation area with different rectangular grid-cell units. Simulation results show that if sufficient amount of training data is available then the proposed method can improve positioning accuracy of 56.74% over the conventional gridbased RF fingerprinting.

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Low-dimensional signal-strength fingerprint-based positioning in wireless LANs

Cited by 74 publications

References 28 publications

RSSI-Based Fingerprint Positioning System for Indoor Wireless Network

RSSI-Based Fingerprint Positioning System for Indoor Wireless Network

Topic detection and compressed classification in Twitter

Genetic algorithm optimized grid-based RF fingerprint positioning in heterogeneous small cell networks

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