Date of Award

12-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Engineering Physics

First Advisor

Kevin C. Gross, PhD

Abstract

Wide area motion imagery (WAMI) sensor technology is advancing rapidly. Increases in frame rates and detector array sizes have led to a dramatic increase in the volume of data that can be acquired. Without a corresponding increase in analytical manpower, much of these data remain underutilized. This creates a need for fast, automated, and robust methods for detecting dim, moving signals of interest. Current approaches fall into two categories: detect-before-track (DBT) and track-before-detect (TBD) methods. The DBT methods use thresholding to reduce the quantity of data to be processed, making real time implementation practical but at the cost of the ability to detect low signal to noise ratio (SNR) targets without acceptance of a high false alarm rate. TBD methods exploit both the temporal and spatial information simultaneously to make detection of low SNR targets possible, but at the cost of computation time. This research seeks to contribute to the near real time detection of low SNR, unresolved moving targets through an extension of earlier work on higher order moments anomaly detection, a method that exploits both spatial and temporal information but is still computationally efficient and massively parallellizable. The MBD algorithm was found to detect targets comparably with leading TBD methods in 1000th the time.

AFIT Designator

AFIT-ENP-DS-18-D-010

Download

Share

COinS