Open-source software and measurement data available at TUT-TLTPOS group

Open-source software available at TUT-TLTPOS group

1.    Simulink Galileo E1 and E5a  baseband transmitter-receiver chain, built within the Galileo Ready Advanced Mass MArket Receiver  (GRAMMAR) project. Before using it, please read the provided license terms. By starting to use it, you agree with the license terms.

How to cite it if used in your work (first reference is for the E1 model, the second one is for the E5 model):

[1] H. Hurskainen, E.S. Lohan, X. Hu, J. Raasakka, and J. Nurmi. ''Multiple Gate Delay tracking structures for GNSS signals and their evaluation with Simulink, SystemC and VHDL'', EURASIP International Journal of Navigation and Observation, volume 2008, article ID 785695, published on-line

[2] D. Alonso de Diego, G.N. Ferrara, J. Nurmi, E.S. Lohan, “Simulink-based open-source simulator for the narrowband interference mitigation in E5a Galileo band”, in Proc. of ESA Colloquium on scientific and fundamental aspects of Galileo, Oct 2015, Germany

2.    Matlab simulator for user indoor mobility models. Created by Wenbo Wang & Pedro Silva, @TUT. How to cite it if used in your work:

[1] W. Wang, P.M.  Figueiredo e Silva, and E.S. Lohan, “Investigations on mobility models and their impact on indoor positioning, 23rd International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL, Nov 2015

1.     A Python-based program which performs RSS-based fingerprinting and clustering-based indoor position estimation with WLAN signals can also be found here. Created by Andrei Cramariuc, @TUT. The link includes also training and estimation WLAN measurement data. The Python-based program has been tested only in Linux; for other operating systems, modifications may be needed.

Research papers to use this data could cite the following paper:

 

[1] Andrei Cramariuc, Heikki Huttunen and Elena Simona Lohan, “Clustering benefits in mobile-centric WiFi positioning in multi-floor buildings”, in Proc. of ICL-GNSS 2016 conference, Barcelona, Spain, Jun 2016

 

 


Measurement data for wireless positioning

2.     Indoor WLAN measurement data in two four-floor buildings for indoor positioning studies.  The data contains the collected RSS values, the Access Points ID (mapped to integer indices) and the (x,y,z) coordinates; both the training data and several tracks for the estimation part are provided. The data can be used with any indoor localization algorithms, such as fingerprinting, path loss probabilistic approaches, Dempster Shaffer decision theory, etc. A Readme file is included. Research papers to use our measurements data could cite the following two papers where also benchmark results based on this data are provides:

[1] S. Shrestha, J. Talvitie, and E.S. Lohan  “Deconvolution-based indoor localization with WLAN signals and unknown access point locations”, in Proc. of IEEE ICL-GNSS, Jun  2013, Italy

[2] J.  Talvitie,  E.S.  Lohan,  and  M.  Renfors,  The  Effect  of  Coverage  Gaps  and  Measurement Inaccuracies in Fingerprinting based Indoor Localization”, in Proc. of IEEE ICL-GNSS conference, Jun 2014, Helsinki, Finland

 

Additional benchmark research results based on these measurements can be found for example in
[3] A. Razavi, M. Valkama, and E.S. Lohan, “K-means Fingerprint Clustering for Low-Complexity Floor Estimation in Indoor Mobile Localization”, in Proc. of IEEE Globecom, LION Workshop, Dec 2015, San Diego, California.

[4] E. Laitinen, J. Talvitie, E.S. Lohan, “On the RSS biases in WLAN-based indoor Positioning”, in Proc. of IEEE ANLN (special session of ICC), Jun 2015, London, UK.

[5] P. Kasebzadeh, G. Seco-Granados, and E.S. Lohan, “Indoor localization via WLAN path-loss models and Dempster-Shafer combining”, in Proc. of IEEE  ICL-GNSS conference, Jun 2014, Helsinki, Finland.

 

 

Return