On structural identification of 2D regression functions for indoor Bluetooth localization

Rene Mayrhofer; Stephan Winkler; Helmut Hlavacs; Michael Affenzeller; Stefan Schneider

doi:10.1007/978-3-642-04772-5_103

On structural identification of 2D regression functions for indoor Bluetooth localization

Rene Mayrhofer, Stephan Winkler, Helmut Hlavacs, Michael Affenzeller, Stefan Schneider

Research Center Hagenberg

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

Abstract

In-door localization of mobile devices is a common problem for many current and future applications, for example to control infrastructure services or for personalized in-building navigation systems. Sufficiently capable Bluetooth support is often available in off-the-shelf mobile devices such as mobile phones, which makes Bluetooth an attractive technology for cheap and widely available in-door localization systems. However, Bluetooth has been optimized to deal with effects of radio frequency transmission such as reflection and multi-path propagation. It therefore produces highly non-linear relationships between the distance of devices and their perceived signal strength. In this paper, we aim to identify these relationships for a specific dataset of 2D device positions using structural identification methods. Driven by an extended genetic algorithm, we aim to find optimal mappings in form of non-linear equations for x and y coordinates, thus producing formal regression functions.

Original language	English
Title of host publication	Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers
Pages	801-808
Number of pages	8
Volume	5717
Edition	1
DOIs	https://doi.org/10.1007/978-3-642-04772-5_103
Publication status	Published - Sept 2009
Event	Twelve International Conference on Computer Aided Systems Theory 2009 - Las Palmas, Spain Duration: 15 Feb 2009 → 20 Feb 2009 http://www.iuctc.ulpgc.es/spain/eurocast2009/index.html

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5717 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Twelve International Conference on Computer Aided Systems Theory 2009
Country/Territory	Spain
City	Las Palmas
Period	15.02.2009 → 20.02.2009
Internet address	http://www.iuctc.ulpgc.es/spain/eurocast2009/index.html

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10.1007/978-3-642-04772-5_103

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Mayrhofer, R., Winkler, S., Hlavacs, H., Affenzeller, M., & Schneider, S. (2009). On structural identification of 2D regression functions for indoor Bluetooth localization. In Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers (1 ed., Vol. 5717, pp. 801-808). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5717 LNCS). https://doi.org/10.1007/978-3-642-04772-5_103

Mayrhofer, Rene ; Winkler, Stephan ; Hlavacs, Helmut et al. / On structural identification of 2D regression functions for indoor Bluetooth localization. Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers. Vol. 5717 1. ed. 2009. pp. 801-808 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In-door localization of mobile devices is a common problem for many current and future applications, for example to control infrastructure services or for personalized in-building navigation systems. Sufficiently capable Bluetooth support is often available in off-the-shelf mobile devices such as mobile phones, which makes Bluetooth an attractive technology for cheap and widely available in-door localization systems. However, Bluetooth has been optimized to deal with effects of radio frequency transmission such as reflection and multi-path propagation. It therefore produces highly non-linear relationships between the distance of devices and their perceived signal strength. In this paper, we aim to identify these relationships for a specific dataset of 2D device positions using structural identification methods. Driven by an extended genetic algorithm, we aim to find optimal mappings in form of non-linear equations for x and y coordinates, thus producing formal regression functions.",

author = "Rene Mayrhofer and Stephan Winkler and Helmut Hlavacs and Michael Affenzeller and Stefan Schneider",

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Mayrhofer, R, Winkler, S, Hlavacs, H, Affenzeller, M & Schneider, S 2009, On structural identification of 2D regression functions for indoor Bluetooth localization. in Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers. 1 edn, vol. 5717, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5717 LNCS, pp. 801-808, Twelve International Conference on Computer Aided Systems Theory 2009, Las Palmas, Spain, 15.02.2009. https://doi.org/10.1007/978-3-642-04772-5_103

On structural identification of 2D regression functions for indoor Bluetooth localization. / Mayrhofer, Rene; Winkler, Stephan; Hlavacs, Helmut et al.
Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers. Vol. 5717 1. ed. 2009. p. 801-808 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5717 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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AU - Affenzeller, Michael

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N2 - In-door localization of mobile devices is a common problem for many current and future applications, for example to control infrastructure services or for personalized in-building navigation systems. Sufficiently capable Bluetooth support is often available in off-the-shelf mobile devices such as mobile phones, which makes Bluetooth an attractive technology for cheap and widely available in-door localization systems. However, Bluetooth has been optimized to deal with effects of radio frequency transmission such as reflection and multi-path propagation. It therefore produces highly non-linear relationships between the distance of devices and their perceived signal strength. In this paper, we aim to identify these relationships for a specific dataset of 2D device positions using structural identification methods. Driven by an extended genetic algorithm, we aim to find optimal mappings in form of non-linear equations for x and y coordinates, thus producing formal regression functions.

AB - In-door localization of mobile devices is a common problem for many current and future applications, for example to control infrastructure services or for personalized in-building navigation systems. Sufficiently capable Bluetooth support is often available in off-the-shelf mobile devices such as mobile phones, which makes Bluetooth an attractive technology for cheap and widely available in-door localization systems. However, Bluetooth has been optimized to deal with effects of radio frequency transmission such as reflection and multi-path propagation. It therefore produces highly non-linear relationships between the distance of devices and their perceived signal strength. In this paper, we aim to identify these relationships for a specific dataset of 2D device positions using structural identification methods. Driven by an extended genetic algorithm, we aim to find optimal mappings in form of non-linear equations for x and y coordinates, thus producing formal regression functions.

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Mayrhofer R, Winkler S, Hlavacs H, Affenzeller M, Schneider S. On structural identification of 2D regression functions for indoor Bluetooth localization. In Computer Aided Systems Theory, EUROCAST 2009 - 12th International Conference, Revised Selected Papers. 1 ed. Vol. 5717. 2009. p. 801-808. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-04772-5_103

On structural identification of 2D regression functions for indoor Bluetooth localization

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