Familiarity Discrimination of Radar Pulses

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). The performance of ARTMAP-FD is tested on radar pulse data obtained in the field, and compared to that of the nearest-neighbor-based NEN algorithm and to a k > 1 extension of NEN

Classification of Incomplete Data Using the Fuzzy ARTMAP Neural Network

The fuzzy ARTMAP neural network is used to classify data that is incomplete in one or more ways. These include a limited number of training cases, missing components, missing class labels, and missing classes. Modifications for dealing with such incomplete data are introduced, and performance is assessed on an emitter identification task using a data base of radar pulsesDefense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409) (S.G. and M.A.R); National Science Foundation (IRI-97-20333) (S.G.); Natural Sciences and Engineerging Research Council of Canada (E.G.); Office of Naval Research (N00014-95-1-0657

Threshold Determination for ARTMAP-FD Familiarity Discrimination

The ARTMAP-FD neural network performs both identification (placing test patterns in classes encountered during training) and familiarity discrimination (judging whether a test pattern belongs to any of the classes encountered during training). ARTMAP-FD quantifies the familiarity of a test pattern by computing a measure of the degree to which the pattern's components lie within the ranges of values of training patterns grouped in the same cluster. This familiarity measure is compared to a threshold which can be varied to generate a receiver operating characteristic (ROC) curve. Methods for selecting optimal values for the threshold are evaluated. The performance of validation-set methods is compared with that of methods which track the development of the network's discrimination capability during training. The techniques are applied to databases of simulated radar range profiles.Advanced Research Projects Agency; Office of Naval Research (N00011-95-1-0657, N00011-95-0109, NOOOB-96-0659); National Science Foundation (IRI-94-01659

A Familiartiy-Based Bound on the Expected Error Rate for Classification with the Fuzzy ARTMAP Neural Network

We obtain a bound on the expected error rate of the fuzzy ARTMAP neural network employed as a classifier. This bound is based on leave-one-out estimation of the classification error, and is analogous to a bound on the expected error rate for support vector machines.Office of Naval Research (N00014-95-1-0409

A Familiartiy-Based Bound on the Expected Error Rate for Classification with the Fuzzy ARTMAP Neural Network

We obtain a bound on the expected error rate of the fuzzy ARTMAP neural network employed as a classifier. This bound is based on leave-one-out estimation of the classification error, and is analogous to a bound on the expected error rate for support vector machines.Office of Naval Research (N00014-95-1-0409

Study to investigate and evaluate means of optimizing the Ku-band combined radar/communication functions for the space shuttle

The performance of the space shuttle orbiter's Ku-Band integrated radar and communications equipment is analyzed for the radar mode of operation. The block diagram of the rendezvous radar subsystem is described. Power budgets for passive target detection are calculated, based on the estimated values of system losses. Requirements for processing of radar signals in the search and track modes are examined. Time multiplexed, single-channel, angle tracking of passive scintillating targets is analyzed. Radar performance in the presence of main lobe ground clutter is considered and candidate techniques for clutter suppression are discussed. Principal system parameter drivers are examined for the case of stationkeeping at ranges comparable to target dimension. Candidate ranging waveforms for short range operation are analyzed and compared. The logarithmic error discriminant utilized for range, range rate and angle tracking is formulated and applied to the quantitative analysis of radar subsystem tracking loops

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 127, April 1974

This special bibliography lists 279 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1974

Monitoring of Incomati River Basin with Remote Sensing

Incomati river basin is located in the continent of South Africa and is shared between three countries of Republic of Mozambique, Republic of South Africa, and the Kingdom of Swaziland. Located in a water-stressed region and shared between three countries it has importance in both sociopolitical and water scarcity aspect. The three countries recently (in 2002) signed an agreement for letting certain amount of water pass through the borders. Accordingly all 3 countries need to implement a monitoring method to evaluate the agreement. This thesis deals with different remote sensing methods for monitoring of water resources in the basin. To do this, after explaining site conditions, different literature has been reviewed and three main remote sensing methods (Optical method, Synthetic Aperture Radar imagery (SAR), Radar Altimetry) are explained briefly. Their advantages and disadvantages and their limitations are discussed. By creating an inventory of available satellites and considering the site specific conditions the use and applicability of those methods to the region are discussed. This paper shows that among the three major Remote Sensing methods, optical method and Synthetic Aperture Radar (SAR) can be used for monitoring of the Incomati basin. Furthermore, the optical method was applied to assess water storage in the region. Some free Landsat images of the region obtained from Global Land Cover Facility (GLCF), www.landcover.org have been analyzed and water storage has been estimated and the results compared with ground truth information. The results obtained from 6 Landsat images showed high accuracy of water storage estimation with an average accuracy of 3.5%