In 2000, the Signal Processing and Speech Communication Laboratory (SPSC Lab) of Graz University of Technology (TU Graz) was founded as a research and education center in nonlinear signal processing and computational intelligence, algorithm engineering, as well as circuits & systems modeling and design. It covers applications in wireless communications, speech/audio communication, and telecommunications.

If you want to learn more about Signal Processing, click: "What is Signal Processing?"

The Research of SPSC Lab addresses fundamental and applied research problems in five scientific areas:


Result of the Month February 2019

Previous results of the month


Setting up indoor localization systems is often excessively time-consuming and labor-intensive, because of the high amount of anchors to be carefully deployed or the burdensome collection of fingerprints. In this work, we present SALMA, a novel low-cost ultra-wideband-based indoor localization system that makes use of only one anchor with minimized calibration and training efforts.
The system leverages the gained insights of our previous works, exploiting multipath reflections of radio signals to enhance positioning performance. To this end, only a crude floor plan is needed which enables SALMA to accurately determine the position of a mobile tag using a single anchor, hence minimizing the infrastructure costs, as well as the setup time.
We implement SALMA on off-the-shelf UWB devices based on the Decawave DW1000 transceiver and show that, by making use of multiple directional antennas, SALMA can also resolve ambiguities due to overlapping multipath components.
An experimental evaluation in an office environment with clear line-of-sight (LOS) has shown that 90% of the position estimates obtained using SALMA exhibit less than 20 cm error, with a median below 8 cm. We further study the performance of SALMA in the presence of obstructed line-of-sight (OLOS) conditions, moving objects and furniture, as well as in highly dynamic environments with several people moving around, showing that the system can sustain decimeter-level accuracy with a worst-case average error below 34 cm.

Michael Rath