Signal Processing and Speech Communication Laboratory
hometheses & projects › Radio-Frequency Simultaneous Localization and Mapping using Transformer-based Deep Learning

Radio-Frequency Simultaneous Localization and Mapping using Transformer-based Deep Learning

Master Thesis
Announcement date
22 Mar 2022
Research Areas


Precise indoor localization using radio signals exchanged between base stations (BSs) and mobile “agents” (radio devices such as mobile phones) remains a challenging problem for several essential applications such as search-and-rescue and autonomous navigation. Simultaneous localization and mapping (SLAM) generates a feature map of the environment and localize the agents within that map in such situations. One promising solution is radio frequency (RF)-SLAM where specular reflections of radio signals off flat surfaces are used to estimate the locations of both an mobile agent and the features representing reflecting surfaces. Model-based state-of-the-art (SOTA) techniques for RF-SLAM formulate and solve a high-dimensional sequential Bayesian estimation problem using factor graphs [1, 2]. All these methods have in common that their robustness and accuracy depends strongly on chosen feature models and how well these features represent the geometry of the environment. If a significant model-mismatch oocurs and the feature cannot be well extracted from the data, these algorithms have the dendency to fail in reconstructing good feature map. To overcome these issues, we propose a deep learning approach in line with [3] to overcome these issues.


  • [1] E. Leitinger, F. Meyer, F. Hlawatsch, K. Witrisal, F. Tufvesson, and M. Z. Win, “A belief propagation algorithm for multipath-based SLAM,” vol. 18, no. 12, pp. 5613–5629, Dec. 2019.
  • [2] E. Leitinger, S. Grebien, and K. Witrisal, “Multipath-based SLAM exploiting AoA and amplitude information,” in Proc. IEEE ICCW-19, Shanghai, China, May 2019, pp. 1–7.
  • [3] J. Pinto, G. Hess, W. Ljungbergh, Y. Xia, H. Wymeersch, and L. Svensson, “Can deep learning be applied to model-based multi-object tracking?” ArXiv e-prints, 2022.

Your Tasks:

  • Design and implement a deep learning algorithm based on Transformer architectures for RF-SLAM in line with the Transformer-based multi-object tracking architecture in [3].
  • Evaluate the performance of the developed method and compare the performance against existing model-based SOTA algorithms [1, 2].

What we expect from you:

  • You are master student in information and computer engineering, electrical engineering, audio engineering or similar studies.
  • Basic knowledge in statistical signal processing, graphical models, and Bayesian modelling are beneficial.
  • Programming knowledge in Matlab and Python


If you are interested and want to know more about it, send me an email to