Graduate Exam Abstract
Chritopher RobbianoPh.D. Preliminary
October 11, 2019, 10:00 am - 11:30 am
ECE conference room
Abstract: Occupancy grids encode for hot spots on a map, represented by a two dimensional grid of
disjoint cells. The problem is to recursively update the probability that each cell in the grid is
occupied, based on a sequence of sensor measurements. In this paper, we provide a new
Bayesian framework for generating these probabilities that does not assume statistical
independence between the occupancy state of grid cells. This approach is formulated through
the use of binary asymmetric channels that capture the errors associated with observing the
occupancy state of a grid cell. Two special cases allow for a faster computation of the
probabilities. The binary valued vectors in this formulation are the output of a physical layer
passed through a detector in an imaging, radar, sonar, or any other sensory system. We
compare the performance of the proposed framework to that of the classical formulation for
occupancy grids. The results show that the proposed framework provides occupancy grids with
lower probability of error rates, higher detection rates, and generally require fewer observations
of the surrounding area to generate an accurate estimate of the map.
Adviser: Edwin Chong
Co-Adviser: Mahmood Azimi
Non-ECE Member: Iuliana Oprea
Member 3: Ali Pezeshki
Addional Members: none
C. Robbiano, A. A. Maciejewski, and E. K. Chong, “An analysis of correlations in student performance in core technical courses at a large public research institution’s electrical and computer engineering department,” in 2018 ASEE Annual Conference & Exposition, 2018.
A. Pezeshki, M. R. Azimi-Sadjadi, and C. Robbiano, “A multiple kernel machine with in-situ learning using sparse representation,” in IJCNN’19. International Joint Conference on Neural Networks. Proceedings, 2019.
M. Azimi-Sadjadi, C. Robbiano, Y. Zhao, and J. Hall, “Incremental dictionary learning for adaptive classification and reconstruction of facial imagery,” in 2019 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019.
Program of Study:
ECE651 Detection Theory
ECE656 Machine Learning
MATH510 Linear Programming
STAT620 Measure Theoretic Probability
STAT720 Probability Theory
ECE516 Information Theory
MATH560 Linear Algebra