High-resolution sonar systems are primarily used for ocean floor surveys and port security operations but produce images of limited resolution. In turn, a sonar-specific methodology is required to detect and classify underwater unexploded ordnance (UXO) using the low-resolution sonar data. After researching and reviewing numerous approaches the Multiple Aspect-Fixed Range Template Matching (MAFR-TM) algorithm was developed. The MAFR-TM algorithm is specifically designed to detect and classify a target of high characteristic impedance in an environment that contains similar shaped objects of low characteristic impedance. MAFR-TM is tested against a tank and field data set collected by the Sound Metrics Corp. DIDSON US300. This thesis document proves the MAFR-TM can detect, classify, orient, and locate a target in the sector-scan sonar images. This paper focuses on the MAFR-TM algorithm and its results. / by Lisa Nicole Brisson. / Thesis (M.S.C.S.)--Florida Atlantic University, 2010. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.

<p>In the modern sensing environment, large numbers of sensor tasking decisions must be made using an increasingly diverse and powerful suite of sensors in order to best fulfill mission objectives in the presence of situationally-varying resource constraints. Sensor management algorithms allow the automation of some or all of the sensor tasking process, meaning that sensor management approaches can either assist or replace a human operator as well as ensure the safety of the operator by removing that operator from a dangerous operational environment. Sensor managers also provide improved system performance over unmanaged sensing approaches through the intelligent control of the available sensors. In particular, information-theoretic sensor management approaches have shown promise for providing robust and effective sensor manager performance.</p><p>This work develops information-theoretic sensor managers for a general static target detection problem. Two types of sensor managers are developed. The first considers a set of discrete objects, such as anomalies identified by an anomaly detector or grid cells in a gridded region of interest. The second considers a continuous spatial region in which targets may be located at any point in continuous space. In both types of sensor managers, the sensor manager uses a Bayesian, probabilistic framework to model the environment and tasks the sensor suite to make new observations that maximize the expected information gain for the system. The sensor managers are compared to unmanaged sensing approaches using simulated data and using real data from landmine detection and unexploded ordnance (UXO) discrimination applications, and it is demonstrated that the sensor managers consistently outperform the unmanaged approaches, enabling targets to be detected more quickly using the sensor managers. The performance improvement represented by the rapid detection of targets is of crucial importance in many static target detection applications, resulting in higher rates of advance and reduced costs and resource consumption in both military and civilian applications.</p> / Dissertation