Open-source software and measurement data 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):
2. Matlab simulator for user indoor mobility models. Created by Wenbo Wang & Pedro Silva, @TUT. How to cite it if used in your work:
 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
3. 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:
 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
4. 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 provided:
 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
 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
benchmark research results based on these measurements can be found for example
 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.
 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.
 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.
5. GSM and UMTS urban measurement data for cellular-based positioning. The data has been collected in Tampere with a Nokia phone. The data is given in Matlab *mat format and it contains a cell variable called BS_grids or BS_grid_set which shows the GPS coordinates (converted in local coordinates, in meter values) and the collected RSS value per transmitter (i.e., BS or Node B).
Example of research results based on these measurements can be found for example in
 J. Talvitie, M. Renfors, E.S. Lohan, and M. Valkama, ”Estimation of Multi-slope Path Loss Models using Hyperbolic Tangent based Basis Functions”, submitted to IEEE Transactions on Antenna and Propagation.
 H. Nurminen, J. Talvitie, S. Ali-Löytty, P. Muller, E.S. Lohan, R. Piché, M. Renfors, “Statistical path loss parameter estimation and positioning using RSS measurements”, Journal of Global Positioning Systems, vol. 12(1), 2013, ISSN 1446-3156, http://www.gnss.com.au/JoGPS/JoGPS_v12_Issue1.html
6. 50000 multi-GNSS constellation points covering the full constellations of GPS, Galileo, Beidou-2/Compass and Glonass. Generated based on a Spectracom GSC-64 simulator. Research papers this data could cite the following paper, where also benchmark results based on this data are provided:
 G.N. Ferrara, J. Nurmi and E.S. Lohan, “Multi-GNSS analysis via Spectracom constellations”, in Proc. of the International Conference on Localization and GNSS (ICL-GNSS 2016), Barcelona, Spain, June 2016.