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Difference between revisions of "Outdoor Precision Object Tracking for Rockfall Experiments"

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[[Category:In progress]]
[[Category:Semester Thesis]]
[[Category:Semester Thesis]]
[[Category:Master Thesis]]
[[Category:Master Thesis]]

Latest revision as of 14:40, 10 November 2020

StoneNode online.jpg

Short Description

Rockfalls and landslides have over the last decades become a serious and frequent hazard, especially due to larger variations in precipitation and temperatures, destabilizing rocky slopes in mountainous regions. Latest simulation tools based on improved mass movement dynamic models allow civil engineers to perform risk assessments and plan mitigation strategies in natural three-dimensional terrain. These tools are based on various models with a large number of parameter that have to be calibrated and evaluated with real-world in-field measurements.[1] For that reason, the WSL Institute for Snow and Avalanche Research SLF in Davos, Switzerland, developed together with the ETH Zurich a rugged low-power multi-sensor node, designed to acquire and log accurate inertial sensor measurements during induced rockfalls. The extension of the sensors scope to mountain forests as well as landslides requires an adaption to more advanced non-vision-based tracking methods.

In this project, the student will conduct a pilot study on tracking of moving objects in rough outdoor environment. (>100m x >50m, forest, mountain slopes, high humidity etc.) The investigation will include, but not limited to, promising radio frequencies technologies, for instance, Global Navigation Satellite Systems (GNSS) with Real Time Kinematics (RTK) and Ultra-Wide-Band (UWB). The goal of this project is to find the best technology which allows track objects in forested regions with high time and special resolution. Afterward, the student will design, implement, and test the complete solution. Depending on the results and project period field tests at SLF test sites can be conducted.

Depending on the applicant's profile, his tasks may involve some of the following:

  • Lab. testing/characterization of RF localization modules and embedded systems: verification of the prototype's characteristics w.r. design specification (simulations), measuring power-consumption, and assessing detection performance in lab. conditions
  • High-level software programming, signal processing, machine learning, sensor fusion, wireless communication
  • programming the circuit for a specific application, field testing, data acquisition
  • PCB design to build a working prototype which includes all the subsystems

(image sources: SLF, ZDF)

Suggested references:

  • [1] A. Caviezel et al., "Design and Evaluation of a Low-Power Sensor Device for Induced Rockfall Experiments," in IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 4, pp. 767-779, April 2018.
  • [2] Alarifi A, Al-Salman A, Alsaleh M, et al. Ultra Wideband Indoor Positioning Technologies: Analysis and Recent Advances. Mihaylova L, Kim B-G, Dogra DP, eds. Sensors (Basel, Switzerland). 2016;16(5):707.

Nice to read and watch:

Status: Available

  • Looking for Semester Project Students (an adaption to a Master Project is possible)
Supervisors: Philipp Mayer, Michele Magno


(not all need to be met by the single candidate)

  • Experience using the laboratory instrumentation - signal generators, oscilloscopes, DAQ cards, Matlab etc.
  • knowledge of microcontroller programming and PC programming (C/C++)
  • basic knowledge or interests in signal processing, wireless communication for localization and machine learning is a plus
  • plus is also knowledge of printed circuit board (PCB) using Altium.


10% Theory
25% Preliminary measurements
30% Implementation
35% Testing

IIS Professor

Luca Benini

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Detailed Task Description


Practical Details