Automatic Target Recognition (ATR) is a demanding application that requires separation of targets from a noisy background in a sequence of images. In our previous work [5] the background was adaptively described using twodimensional filters designed by Principle Component Analysis on sampled two-dimensional image patches. Significant improvements in performance have been obtained by decision le...

This paper presents an innovative hierarchical feedback adaptation method that efficiently controls the dynamic QoS behavior of real-time distributed data-flow applications, such as sensor-based data streams or mission-critical command and control applications. We applied this method in the context of the Real Time Adaptive Resource Management 1 system, a middleware architecture for resource ma...

A tertiary goal, which we plan to explore in depth in future research, is the integration of image compression with automated target recognition (ATR). Two cases are of interest to this study. First, we have shown that one can achieve increased processing efficiency by applying specially configured ATR operations to compressed imagery [Sch97c,d]. Second, we have developed techniques for prefilt...

The capability to exploit multiple sources of information is of fundamental importance in a battlefield scenario. Information obtained from different sources, and separated in space and time, provide the opportunity to exploit diversities in order to mitigate uncertainty. For the specific challenge of Automatic Target Recognition (ATR) from radar platforms, both channel (e.g. polarization) and ...

This paper presents a novel framework for evaluating Target Detection and Classification algorithms and concepts of operations based on Augmented Reality (AR). Real sonar images and synthetic target models are used to generate a ground-truthed AR theatre of operation. The detection/classification results of the human operator or Automatic Target Recognition (ATR) algorithm to be evaluated are t...

MIT Lincoln Laboratory is responsible for developing the ATR (automatic target recognition) system for the DARPA-sponsored SAIP program; the baseline ATR system recognizes 10 GOB (ground order of battle) targets; the enhanced version of SAIP requires the ATR system to recognize 20 GOB targets. This paper presents ATR performance results for 10and 20-target MSE classifiers using highresolution S...

Cost-sensitive classification is one of the hottest research topics in data mining and machine learning. It is prevalent in many applications, such as Automatic Target Recognition (ATR), medical diagnosis, etc. However, the data in practice may be inconsistent due to dimensional reduction operation or other pre-processing, yet it is not clear how the inconsistent data affects cost-sensitive lea...

Mechanical systems or robots to assist landmine detection are expected to greatly improve quality of humanitarian demining tasks. These new systems could provide: i) safer operation; ii) advanced methods for automatic target recognition and discrimination; iii) consistent performance with less influence of “human-factors”; iv) better detection performance, i.e., higher probability of detection ...

Super-resolution of signals and images can improve the automatic detection and recognition of objects of interest. However, the uncertainty associated with this process is not often taken into consideration. This is important because the processing of noisy signals can result in spurious estimates of the scene content. This paper reviews a variety of super-resolution techniques and presents two...

The UGV / Demo II program, begun in 1992, developed and matured those navigation and automatic target recognition technologies critical for the development of supervised, autonomous ground vehicles capable of performing military scout missions with a minimum of human oversight. The program culminated with a highly successful series of field exercises performed by soldiers at Ft. Hood, Texas ove...