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Articles 31 - 43 of 43
Full-Text Articles in Engineering
Airborne Radar Interference Suppression Using Adaptive Three-Dimensional Techniques, Todd B. Hale
Airborne Radar Interference Suppression Using Adaptive Three-Dimensional Techniques, Todd B. Hale
Theses and Dissertations
This research advances adaptive interference suppression techniques for airborne radar, addressing the problem of target detection within severe interference environments characterized by high ground clutter levels, levels, noise jammer infiltration, and strong discrete interferers. Two-dimensional (2D) Space-Time Adaptive Processing (STAP) concepts are extended into three-dimensions (3D) by casting each major 2D STAP research area into a 3D framework. The work first develops an appropriate 3D data model with provisions for range ambiguous clutter returns. Adaptive 3D development begins with two factored approaches, 3D Factored Time-Space (3D-FTS) and Elevation-Joint Domain Localized (Elev-JDL). The 3D adaptive development continues with optimal techniques, i.e., …
An Examination Of Latency And Degradation Issues In Unmanned Combat Aerial Vehicle Environments, Shane A. Dougherty
An Examination Of Latency And Degradation Issues In Unmanned Combat Aerial Vehicle Environments, Shane A. Dougherty
Theses and Dissertations
Since the multidimensional knapsack problems are NP-hard problems, the exact solutions of knapsack problems often need excessive computing time and storage space. Thus, heuristic approaches are more practical for multidimensional knapsack problems as problems get large. This thesis presents the results of an empirical study of the performance of heuristic solution procedures based on the coefficients correlation structures and constraint slackness settings. In this thesis, the three representative greedy heuristics, Toyoda, Senju and Toyoda, and Loulou and Michaelides methods, are studied. The purpose of this thesis is to explore which heuristic of the three representative greedy heuristics perform best under …
Air Vehicle Optimal Trajectories For Radar Exposure Minimization, Michael C. Novy
Air Vehicle Optimal Trajectories For Radar Exposure Minimization, Michael C. Novy
Theses and Dissertations
This study addresses the problem of analyzing the single vehicle path planning problem for radar exposure minimization. The calculus of Variations and optimal control are applied to formulate the cost function and numerical algorithms are used to solve for the optimal paths. Cost sensitivity to path length is analyzed for flight against one radar; a second radar is then included in the formulation and the optimal path for flight between the radars is found for cases of equal and unequal radar power. The costs of the optimal path, direct path, and Voronoi diagram-generated paths are compared. Results indicate low sensitivity …
Embedding A Reactive Tabu Search Heuristic In Unmanned Aerial Vehicle Simulations, Joel L. Ryan
Embedding A Reactive Tabu Search Heuristic In Unmanned Aerial Vehicle Simulations, Joel L. Ryan
Theses and Dissertations
We apply a Reactive Tabu Search (RTS) heuristic within a discrete event simulation to solve routing problems for Unmanned Aerial Vehicles (UAVs). Our formulation represents this problem as a multiple Traveling Salesman Problem with time windows (mTSPTW), with the objective of attaining a specified level of target coverage using a minimum number of vehicles. Incorporating weather and probability of UAV survival at each target as random inputs, the RTS heuristic in the simulation searches for the best solution in each realization of the problem scenario in order to identify those routes that are robust to variations in weather, threat, or …
Maximum Likelihood Estimation Of Exponentials In Unknown Colored Noise For Target In Identification Synthetic Aperture Radar Images, Matthew P. Pepin
Maximum Likelihood Estimation Of Exponentials In Unknown Colored Noise For Target In Identification Synthetic Aperture Radar Images, Matthew P. Pepin
Theses and Dissertations
This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood ML estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar SAR data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations exponentials frequencies are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques …
Maximum Likelihood Estimation Of Exponentials In Unknown Colored Noise For Target Identification In Synthetic Aperture Radar Images, Matthew P. Pepin
Maximum Likelihood Estimation Of Exponentials In Unknown Colored Noise For Target Identification In Synthetic Aperture Radar Images, Matthew P. Pepin
Theses and Dissertations
This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood (ML) estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar (SAR) data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations (exponentials frequencies) are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques …
Non-Imaging Infrared Spectral Target Detection, Matthew R. Whiteley
Non-Imaging Infrared Spectral Target Detection, Matthew R. Whiteley
Theses and Dissertations
Automatic detection of time-critical mobile targets using spectral-only infrared radiance data is explored. A quantification of the probability of detection, false alarm rate, and total error rate associated with this detection process is provided. A set of classification features is developed for the spectral data, and these features are utilized in a Bayesian classifier singly and in combination to provide target detection. The results of this processing are presented and sensitivity of the class separability to target set, target configuration, diurnal variations, mean contrast, and ambient temperature estimation errors is explored. This work introduces the concept of atmospheric normalization of …
Perceptual Based Image Fusion With Applications To Hyperspectral Image Data, Terry A. Wilson
Perceptual Based Image Fusion With Applications To Hyperspectral Image Data, Terry A. Wilson
Theses and Dissertations
Development of new imaging sensors has created a need for image processing techniques that can fuse images from different sensors or multiple images produced by the same sensor. The methods presented here focus on combining image data from the Airborne Visual and Infrared Imaging Spectrometer (AVIRIS) hyperspectral sensor into a single or smaller subset of images while maintaining the visual information necessary for human analysis. Three hierarchical multi-resolution image fusion techniques are implemented and tested using the AVIRIS image data and test images that contain various levels of correlated or uncorrelated noise. Two of the algorithms are published fusion methods …
Multispectral Detection Of Ground Targets In Highly Correlated Backgrounds, Jason E. Thomas
Multispectral Detection Of Ground Targets In Highly Correlated Backgrounds, Jason E. Thomas
Theses and Dissertations
Multispectral detection methods attempt to discriminate targets in a dominant clutter background using multiple images of the same real-world scene taken in different narrow spectral bands in the infrared. Detection is possible due to the empirically observed phenomenon that the radiance of man-made objects, such as a tank or truck, often lies off the main spectral correlation axis of that of natural backgrounds. Radiometric measurements of several vehicles and a tree canopy background taken over three days in June. 1994 were used to examine the factors affecting multispectral detection. Results clearly showed that the processes which provide for higher spectral …
Classification Of Ultra High Range Resolution Radar Using Decision Boundary Analysis, Christopher L. Eisenbies
Classification Of Ultra High Range Resolution Radar Using Decision Boundary Analysis, Christopher L. Eisenbies
Theses and Dissertations
This thesis examines the discrimination of targets with Ultra High Range Resolution (UHRR) radar data. Using these measured signals from frontal aspect angles of four aircraft classes, the baseline performance of the Adaptive Gaussian Classifier (AGC) is tested with respect to aligning exemplars to templates. Alignment plays a crucial role in the AGC's classification performance which can degrade by 11% for a target class. The AGC is compared to non-parametric classifiers, but no statistically significant degradation of performance is found. Data separability is analyzed by hounding the Bayes error. The data is well separated in a statistical sense. A feature …
Processing Of Wide-Angle Synthetic Aperture Radar Signals For Detection Of Obscured Ground Targets, Richard J. Sumner
Processing Of Wide-Angle Synthetic Aperture Radar Signals For Detection Of Obscured Ground Targets, Richard J. Sumner
Theses and Dissertations
This thesis investigates advanced processing techniques for the detection of radar targets in the presence of clutter. It is assumed that the radar data available consist of multi-aspect angle, fully polarimetric Synthetic Aperture Radar (SAR) images. Various techniques are introduced and tested on available SAR data. These techniques attempt to exploit the multi-aspect angles in order to extract target characteristics not available in any single image. SAR images are manipulated in such a way to decrease the probability of false alarms in the target detection process. Target detection performance of the techniques is presented and compared. The techniques are shown …
Application Of Sequence Comparison Methods To Multisensor Data Fusion And Target Recognition, Edmund W. Libby
Application Of Sequence Comparison Methods To Multisensor Data Fusion And Target Recognition, Edmund W. Libby
Theses and Dissertations
This research addresses methods for exploiting the joint likelihood of observed kinematic and nonkinematic (sensor signature) physical events to improve dynamic object and target recognition. A principal direction is the application of dynamic programming sequence comparison techniques to condition matching of object signatures to known models according to observed kinematics. A second direction is the application of kinematic/aspect-angle Kalman filter trackers to condition kinematic tracking according to observed signatures. These conditioning processes dramatically reduce ambiguity in object recognition, and can be used together or separately to allow computation of a posterior probabilities of object class membership using Bayesian methods. Proposals …
High Range Resolution Radar Target Identification Using The Prony Model And Hidden Markov Models, Mark R. Dewitt
High Range Resolution Radar Target Identification Using The Prony Model And Hidden Markov Models, Mark R. Dewitt
Theses and Dissertations
Fully polarized Xpatch signatures are transformed to two left circularly polarized signals. These two signals are then filtered by a linear FM pulse compression ('chirp') transfer function, corrupted by AWGN, and filtered by a filter matched to the 'chirp' transfer function. The bandwidth of the 'chirp' radar is about 750 MHz. Range profile feature extraction is performed using the TLS Prony Model parameter estimation technique developed at Ohio State University. Using the Prony Model, each scattering center is described by a polarization ellipse, relative energy, frequency response, and range. This representation of the target is vector quantized using a K-means …