Super-Resolution Processing and Fusion of Multisensor Data for Advanced Target Surveillance and Tracking
Abstract
Design of advanced surveillance and tracking systems typically employ multiple sensors which can provide large amounts of useful data to detect, identify and track targets of interest. Attempts at intelligent utilization of data for optimizing the processing efficiency in such multi-sensor operations require novel processing methods which need to be carefully tailored due to the disparate forms of data and the disparity in the resolution achievable from these sensors. This project was primarily aimed at the super-resolution processing of imagery data to improve the resolution in acquired images so that any problems arising from the disparity in resolution levels can be mitigated and efficient synthesis of fusion mechanisms can be developed. Specific investigations that were conducted as part of this project included: (i) A detailed analysis of resolution challenges addressing several important questions such as how to quantify resolution in an image (acquired or processed), (ii) development of systematic digital processing algorithms obtained by employing a statistical optimization framework for achieving resolution enhancement and super-resolution, and (iii) performance evaluation studies that included results of processing both simulated image data and results of processing PMMW data acquired from the radiometers being built by the Air Force Wright Laboratory Armament Directorate. The major accomplishments and research advances made in this project through rigorous mathematical analysis and extensive simulation experiments are outlined in this report.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 31, 2000
- Accession Number
- ADA382829
Entities
People
- Malur K. Sundareshan
Organizations
- University of Arizona