Input of Factor Graphs into the Detection, Classification, and Localization Chain and Continuous Active SONAR in Undersea Vehicles

Abstract

The focus of this thesis is to implement factor graphs into the problem of detection, classification, and localization (DCL) of underwater objects using active SOund Navigation And Ranging (SONAR). A factor graph is a bipartite graphical representation of the decomposition of a particular function. Messages are passed along the edges connecting factor and variable nodes, on which, a message passing algorithm is applied to compute the posterior probabilities at a particular node.

This thesis addresses two issues. In the first section, the formulation of factor graphs for each section of the DCL chain required followed by their closed-form solutions. For the detector, the factor graph determines if the signal is a detection or simply noise. In the classifier, it outputs the probability for the elements in the class. Last, when using a factor graph for the tracker, it gives the estimated state of the object being tracked.

The second part concentrates on the application to Continuous Active SONAR (CAS). When using CAS, a bistatic configuration is used allowing for a more rapid update rate where two unmanned underwater vehicles (UUVs) are used as the receiver and transmitter. The goal is to evaluate CAS's effectiveness to determine if the tracking accuracy improves as the transmit interval decreases. If CAS proves to be more efficient in target tracking, the next objective is to determine which messages sent between the two UUVs are most beneficial. To test this, a particle filter simulation is used.