Access Point topology evaluation and optimization based on Cramér-Rao Lower Bound for WLAN indoor positioning

Laitinen, Elina; Lohan, Elena Simona

doi:10.1109/icl-gnss.2016.7533850

Cited by 10 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To investigate the deviations from the ground truth in more depth the Cramér-Rao Lower Bound (CRLB) was calculated. The CRLB is defined as the inverse of the Fisher information matrix (FIM)

[ 60 , 61 , 62 ]:

…”

Section: Localization In the On-line Positioning Phasementioning

confidence: 99%

Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Retscher

Leb

2021

Sensors

View full text Add to dashboard Cite

A guidance and information service for a University library based on Wi-Fi signals using fingerprinting as chosen localization method is under development at TU Wien. After a thorough survey of suitable location technologies for the application it was decided to employ mainly Wi-Fi for localization. For that purpose, the availability, performance, and usability of Wi-Fi in selected areas of the library are analyzed in a first step. These tasks include the measurement of Wi-Fi received signal strengths (RSS) of the visible access points (APs) in different areas. The measurements were carried out in different modes, such as static, kinematic and in stop-and-go mode, with six different smartphones. A dependence on the positioning and tracking modes is seen in the tests. Kinematic measurements pose much greater challenges and depend significantly on the duration of a single Wi-Fi scan. For the smartphones, the scan durations differed in the range of 2.4 to 4.1 s resulting in different accuracies for kinematic positioning, as fewer measurements along the trajectories are available for a device with longer scan duration. The investigations indicated also that the achievable localization performance is only on the few meter level due to the small number of APs of the University own Wi-Fi network deployed in the library. A promising solution for performance improvement is the foreseen usage of low-cost Raspberry Pi units serving as Wi-Fi transmitter and receiver.

show abstract

“…To investigate the deviations from the ground truth in more depth the Cramér-Rao Lower Bound (CRLB) was calculated. The CRLB is defined as the inverse of the Fisher information matrix (FIM)

[ 60 , 61 , 62 ]:

…”

Section: Localization In the On-line Positioning Phasementioning

confidence: 99%

Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Retscher

Leb

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Although in each case, the optimization objective varies, two general approaches can be distinguished. In the first one, the goal is to minimize the expected error of the positioning estimation (Baala et al, 2009, Sharma et al, 2010, He et al, 2011, Ficco et al, 2013, Li et al, 2015, Laitinen , Lohan, 2016, Voronov, 2017. However, it can be argued that making an accurate prediction of the error of a positioning estimation is quite challenging due to the complexity of properly modelling the error's sources themselves.…”

Section: Introductionmentioning

confidence: 99%

“…Although this idea has been favoured by the latest papers on this field (Meng et al, 2012, Chen et al, 2014, Du , Yang, 2017, Alsmady , Awad, 2017, Eldeeb et al, 2018, it may still have some points of criticism, mainly related to the way this distance is measured. It is worth mentioning that in many cases, the objective of the studies was to also optimize the number of nodes needed to be deployed (and thus the installation cost) (He et al, 2011, Ficco et al, 2013, Chen et al, 2014, Li et al, 2015, Laitinen , Lohan, 2016.…”

Section: Introductionmentioning

confidence: 99%

Placement Optimization of Positioning Nodes: Maximizing the Distinction of Indoor Zones

Xenakis

Meijers

Verbree

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

<p><strong>Abstract.</strong> The performance of an indoor positioning system is highly related to the placement of the transmitting nodes that are used as references for the positioning estimations. In this paper, we propose a methodology that can be used to optimize such a deployment and thus, increase the performance of an indoor positioning system that a) is based on Received Signal Strength (RSS) fingerprinting and b) is orientated towards providing location or zone estimations instead of exact positioning. The optimization process involves 4 fundamental components. Firstly, the modelling of the obstructions in the indoor environment and also the zone modelling. Then, the definition of the performance metric that can be used to evaluate each different deployment scenario, in which case, our proposed metric considers the separation area and distances between the zones in the RSS vector space. The third component is the radio propagation model, required for simulating the RSSs from each node, where a model based on the ray tracing technique is selected. Finally, the last component is the selection of the optimization function that will control and drive the whole optimization process by selecting which deployment schemes to evaluate. For that, the utilization of a Genetic Algorithm is proposed. Although the evaluation of this methodology is outside the paper’s scope, the key factors affecting the optimization performance the most, are expected to be a) the accuracy of the used indoor model and radio propagation model and b) the exact implementation of the optimization function.</p>

show abstract

“…In [21], a map-assisted AP placement algorithm has been proposed, and an accuracy within 3 m of 71% has been achieved. In [22], AP topology has been evaluated and optimized utilizing Cramér-Rao Lower Bound calculation method. The deploying schemes in [20,21,22] have an instructional significance to the implementation of APs when a new indoor WLAN based positioning system is set up.…”

Section: Introductionmentioning

confidence: 99%

“…In [22], AP topology has been evaluated and optimized utilizing Cramér-Rao Lower Bound calculation method. The deploying schemes in [20,21,22] have an instructional significance to the implementation of APs when a new indoor WLAN based positioning system is set up. However, the APs’ deployments in most positioning conditions are generally fixed together with the construction of these infrastructures.…”

Section: Introductionmentioning

confidence: 99%

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

Liu

Deng

2016

Sensors

View full text Add to dashboard Cite

Indoor positioning technologies has boomed recently because of the growing commercial interest in indoor location-based service (ILBS). Due to the absence of satellite signal in Global Navigation Satellite System (GNSS), various technologies have been proposed for indoor applications. Among them, Wi-Fi fingerprinting has been attracting much interest from researchers because of its pervasive deployment, flexibility and robustness to dense cluttered indoor environments. One challenge, however, is the deployment of Access Points (AP), which would bring a significant influence on the system positioning accuracy. This paper concentrates on WLAN based fingerprinting indoor location by analyzing the AP deployment influence, and studying the advantages of coordinate-based clustering compared to traditional RSS-based clustering. A coordinate-based clustering method for indoor fingerprinting location, named Smallest-Enclosing-Circle-based (SEC), is then proposed aiming at reducing the positioning error lying in the AP deployment and improving robustness to dense cluttered environments. All measurements are conducted in indoor public areas, such as the National Center For the Performing Arts (as Test-bed 1) and the XiDan Joy City (Floors 1 and 2, as Test-bed 2), and results show that SEC clustering algorithm can improve system positioning accuracy by about 32.7% for Test-bed 1, 71.7% for Test-bed 2 Floor 1 and 73.7% for Test-bed 2 Floor 2 compared with traditional RSS-based clustering algorithms such as K-means.

show abstract

Access Point topology evaluation and optimization based on Cramér-Rao Lower Bound for WLAN indoor positioning

Cited by 10 publications

References 15 publications

Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Development of a Smartphone-Based University Library Navigation and Information Service Employing Wi-Fi Location Fingerprinting

Placement Optimization of Positioning Nodes: Maximizing the Distinction of Indoor Zones

Coordinate-Based Clustering Method for Indoor Fingerprinting Localization in Dense Cluttered Environments

Contact Info

Product

Resources

About