Comparison of Filter Systems in a CIR-based 2-dimensional Location Cell

Status: open
Supervisor: Horst Hellbrück , Sven Ole Schmidt
Student: open

Topic

In Industry 4.0 Applications the product needs to be located many times. Since manual scans like barcodes are inconvenient and can lead to massive errors, non-manual location scenarios take place in industrial halls. In many wireless location systems, the transmission time leads to the distance between the transmitting anchor and the receiving tag. Therefore, using the Channel Impulse Response instead of multiple anchors is a big topic in the recent location research. The analysis of the transmission environment including errors and distortion is important and exact localization of the tag depends on good models.

Details

The aim is to simulate a 2-dimensional rectangular room. With respect to this room, create a position-depending channel impulse response, which is superimposed with Gaussian noise. Based on this channel impulse responses, the position has to be estimated with the aid of particle filters and Kalman filters respectively. Besides signal processing theory, this includes familiarization with multipath spreading and channel impulse responses, including spatial aspects of space geometry.

Deliverables

  • Documentation including a theoretical and experimental background of the scenarios and an evaluation
  • Well-documented simulation scripts

Tasks

  • Get to know multipath transmission channels, like Rayleigh and Rice fading channels
  • Development of a simple 2-dimensional room geometry, yielding a position-corresponding noisy channel impulse response (Line-of-sight, 1st order and 2nd order reflections)
  • Simulate Monte-Carlo-based channel impulse responses for one unknown position
  • Estimate this position by particle filters and Kalman filters
  • Evaluate the results and decide by a self-chosen metric, which performs the best

Requirements

  • Good knowledge of Matlab programming
  • Basic knowledge in wireless communication (PHY-Layer)
  • Signal processing knowledge including particle filters and Kalman filters is desirable
  • Interest in theoretical simulations
  • Willingness to familiarize yourself with signal transmission and processing
  • Independent and self-organized working