Experiments and samples in Multidimensional Scaling based localization

Python script(s) for various MDS algorithms and experiments

Installation

git clone https://github.com/mkoledoye/mds_experiments/
cd mds_experiments
pip install -r requirements.txt

Experiments

Run any of the experiments with:

python run.py --exp_no EXP_NO --nruns NRUNS

NRUNS is the number of times an experiment should be repeated each initialized with a random configuration X. Pass NRUNS >= 100 to have a sufficient amount of trials.

EXP_NO values are described below.

The experiments are dividing into two groups:

• Comparisons:
  compares the performance of MDS variants changing the amount of noise (EXP_NO=1) or number of anchors (EXP_NO=2).
• Missing Data:
  check the effects of missing data in the distance matrix varying the amount of noise (EXP_NO=3) or the number of tags (EXP_NO=4).

Animation

A sample animation of the computed configuration using any of the MDS variants can be viewed by running:

python animation.py

_{NOTE: The animation requires Python 3}

Relevant publications

1. M. A. Koledoye, T. Facchinetti and L. Almeida, “MDS-based localization with known anchor locations and missing tag-to-tag distances,” 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Limassol, 2017, pp. 1-4.
   doi: 10.1109/ETFA.2017.8247768
2. C. Di Franco, E. Bini, M. Marinoni and G. C. Buttazzo, “Multidimensional scaling localization with anchors,” 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Coimbra, 2017, pp. 49-54.
   doi: 10.1109/ICARSC.2017.7964051