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Articles 1 - 5 of 5
Full-Text Articles in Signal Processing
Autoassociative-Heteroassociative Neural Network, Claudia V. Kropas-Hughes, Steven K. Rogers, Mark E. Oxley, Matthew Kabrisky
Autoassociative-Heteroassociative Neural Network, Claudia V. Kropas-Hughes, Steven K. Rogers, Mark E. Oxley, Matthew Kabrisky
AFIT Patents
An efficient neural network computing technique capable of synthesizing two sets of output signal data from a single input signal data set. The method and device of the invention involves a unique integration of autoassociative and heteroassociative neural network mappings, the autoassociative neural network mapping enabling a quality metric for assessing the generalization or prediction accuracy of the heteroassociative neural network mapping.
Unequal A Priori Probability Multiple Hypothesis Testing In Space Domain Awareness With The Space Surveillance Telescope, Tyler J. Hardy, Stephen C. Cain, Travis F. Blake
Unequal A Priori Probability Multiple Hypothesis Testing In Space Domain Awareness With The Space Surveillance Telescope, Tyler J. Hardy, Stephen C. Cain, Travis F. Blake
Faculty Publications
This paper investigates the ability to improve Space Domain Awareness (SDA) by increasing the number of detectable Resident Space Objects (RSOs) from space surveillance sensors. With matched filter based techniques, the expected impulse response, or Point Spread Function (PSF), is compared against the received data. In the situation where the images are spatially undersampled, the modeled PSF may not match the received data if the RSO does not fall in the center of the pixel. This aliasing can be accounted for with a Multiple Hypothesis Test (MHT). Previously, proposed MHTs have implemented a test with an equal a priori prior …
Detecting Near-Earth Objects Using Cross-Correlation With A Point Spread Function, Anthony P. O'Dell
Detecting Near-Earth Objects Using Cross-Correlation With A Point Spread Function, Anthony P. O'Dell
Theses and Dissertations
This thesis describes a process to help discover Near-Earth Objects (NEOs) of larger than 140 meters in diameter from ground based telescopes. The process involves using Nyquist sampling rate to take data from a ground-based telescope and measuring the atmospheric seeing parameter, r0, at the time of data collection. r0 is then used to create a point spread function (PSF) for a NEO at the visual magnitude limit of the telescope and exposure time. This PSF is cross-correlated with the Nyquist sampling rate image from the telescope to reduce the noise and therefore increase the detection probability of …
Statistical Methods For Image Registration And Denoising, Matthew D. Sambora
Statistical Methods For Image Registration And Denoising, Matthew D. Sambora
Theses and Dissertations
This dissertation describes research into image processing techniques that enhance military operational and support activities. The research extends existing work on image registration by introducing a novel method that exploits local correlations to improve the performance of projection-based image registration algorithms. The dissertation also extends the bounds on image registration performance for both projection-based and full-frame image registration algorithms and extends the Barankin bound from the one-dimensional case to the problem of two-dimensional image registration. It is demonstrated that in some instances, the Cramer-Rao lower bound is an overly-optimistic predictor of image registration performance and that under some conditions, the …
The Navigation Potential Of Signals Of Opportunity-Based Time Difference Of Arrival Measurements, Kenneth A. Fisher
The Navigation Potential Of Signals Of Opportunity-Based Time Difference Of Arrival Measurements, Kenneth A. Fisher
Theses and Dissertations
This research introduces the concept of navigation potential, NP, to quantify the intrinsic ability to navigate using a given signal. NP theory is a new, information theory-like concept that provides a theoretical performance limit on estimating navigation parameters from a received signal that is modeled through a stochastic mapping of the transmitted signal and measurement noise. NP theory is applied to SOP-based TDOA systems in general as well as for the Gaussian case. Furthermore, the NP is found for a received signal consisting of the transmitted signal, multiple delayed and attenuated replicas of the transmitted signal, and measurement noise. Multipath-based …