Graduate Exam Abstract
Christopher RobbianoPh.D. Final
November 23, 2020, 3:30 pm - 5:30 pm
Path Planning for Detection and Classification of Underwater Targets using Sonar
Abstract: The work presented in this dissertation focuses on choosing an optimal path for performing sequential detection and classification state estimation to identify potential underwater targets using sonar imagery. The detection state estimation falls under the occupancy grid framework, modeling the relationship between occupancy state of grid cells and sensor measurements, and allows for the consideration of statistical dependence between the occupancy state of each grid cell in the map. This is in direct contrast to the classical formulations of occupancy grid frameworks, in which the occupancy state of each grid cell is considered statistically independent. The new method provides more accurate estimates, and occupancy grids estimated with this method typically converge with fewer measurements. The classification state estimation utilises a Dirichlet-Categorical model and a one-step classifier to perform efficient updating of the classification state estimate for each grid cell. To show the performance capabilities of the developed sequential state estimation methods, they are applied to sonar systems in littoral areas in which targets lay on the seafloor, could be proud, partially or fully buried.
Additionally, a new approach to the active perception problem, which seeks to select a series of sensing actions that provide the maximal amount of information to the system, is developed. This new approach leverages the aforementioned sequential state estimation techniques to develop a set of information-theoretic cost functions that can be used for optimal sensing action selection. A path planning cost function is developed, defined as the mutual information between the aforementioned state variables before and after a measurement. The cost function is expressed in closed form by considering the prior and posterior distributions of the state variables. Choice of the optimal sensing actions is performed by modeling the path planning as a Markov decision problem, and solving it with the rollout algorithm.
This work, supported by the Office of Naval Research (ONR), is intended to develop a suite of interactive sensing algorithms to autonomously command an autonomous underwater vehicle (AUV) for the task of detection and classification of underwater mines, while choosing an optimal navigation route that increases the quality of the detection and classification state estimates.
Adviser: Edwin Chong
Co-Adviser: Mo Azimi
Non-ECE Member: Iuliana Oprea, Math
Member 3: Ali Pezeshki, ECE
Addional Members: N/A
Robbiano, Christopher, et al. "Bayesian learning of occupancy grids." IEEE Transactions on Intelligent Transportation Systems (2020).
Robbiano, Christopher, Edwin KP Chong, and Mahmood R. Azimi-Sadjadi. "Information-Theoretic Approach to Navigation for Efficient Detection and Classification of Underwater Objects." arXiv preprint arXiv:2007.05072 (2020).
Robbiano, Christopher, Anthony A. Maciejewski, and Edwin KP Chong. "Nonparametric Analysis of the Effect of Knowledge Integration Activities on Third-Year Undergraduate Performance." IEEE Transactions on Education (2020).
Azimi-Sadjadi, Mahmood R., et al. "Incremental Dictionary Learning For Adaptive Classification And Reconstruction Of Facial Imagery." 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2019.
Pezeshki, Ali, Mahmood R. Azimi-Sadjadi, and Christopher Robbiano. "A Multiple Kernel Machine with Incremental Learning using Sparse Representation." 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019.
Robbiano, Christopher, Anthony A. Maciejewski, and Edwin KP Chong. "An Analysis of Correlations in Student Performance in Core Technical Courses at a Large Public Research Institution’s Electrical and Computer Engineering Department." 2018 ASEE Annual Conference & Exposition. 2018.
Program of Study: