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Articles 1 - 16 of 16
Full-Text Articles in Signal Processing
A Nonparametric Approach To Segmentation Of Ladar Images, Eric A. Buschelman
A Nonparametric Approach To Segmentation Of Ladar Images, Eric A. Buschelman
Theses and Dissertations
The advent of advanced laser radar (ladar) systems that record full-waveform signal data has inspired numerous inquisitions which aspire to extract additional, previously unavailable, information about the illuminated scene from the collected data. The quality of the information, however, is often related to the limitations of the ladar camera used to collect the data. This research project uses full-waveform analysis of ladar signals, and basic principles of optics, to propose a new formulation for an accepted signal model. A new waveform model taking into account backscatter reflectance is the key to overcoming specific deficiencies of the ladar camera at hand, …
Modeling The Effects Of The Local Environment On A Received Gnss Signal, Marshall E. Haker
Modeling The Effects Of The Local Environment On A Received Gnss Signal, Marshall E. Haker
Theses and Dissertations
There is an ongoing need in the GNSS community for the development of high-fidelity simulators which generate data that replicates what can truly be expected from a challenging environment such as an urban canyon or an indoor environment. The algorithm developed for use in the research in this dissertation, the Signal Decomposition and Parameterization Algorithm (SDPA), is presented in order to respond to this need. This algorithm is designed to decompose a signal received using a GNSS recording and playback system and output parameters that can be used to reconstruct the effects on the signal of the environment local to …
Resolution Study Of A Hyperspectral Sensor Using Computed Tomography In The Process Of Noise, Samuel V. Mantravadi
Resolution Study Of A Hyperspectral Sensor Using Computed Tomography In The Process Of Noise, Samuel V. Mantravadi
Theses and Dissertations
Recently, a new type of hyperspectral imaging sensor has been proposed which simultaneously records both spectral data and multiple spatial dimensions. Unlike dispersive imaging spectrometers, chromo-tomographic hyperspectral imaging sensors (CTHIS) record two spatial dimensions as well as a spectral dimension using computed tomography (CT) techniques with only a finite number of spatially-spectrally diverse images. To date, the factors affecting resolution of these sensors have not been examined. This research examines factors affecting resolution, specifically the number of the focus planes needed to resolve a particular object calculated from a theoretical lower bound, determine a method of reconstructing a hyperspectral object …
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Towards The Mitigation Of Correlation Effects In The Analysis Of Hyperspectral Imagery With Extension To Robust Parameter Design, Jason P. Williams
Theses and Dissertations
Standard anomaly detectors and classifiers assume data to be uncorrelated and homogeneous, which is not inherent in Hyperspectral Imagery (HSI). To address the detection difficulty, a new method termed Iterative Linear RX (ILRX) uses a line of pixels which shows an advantage over RX, in that it mitigates some of the effects of correlation due to spatial proximity; while the iterative adaptation from Iterative Linear RX (IRX) simultaneously eliminates outliers. In this research, the application of classification algorithms using anomaly detectors to remove potential anomalies from mean vector and covariance matrix estimates and addressing non-homogeneity through cluster analysis, both of …
Implementation Of Branch-Point-Tolerant Wavefront Reconstructor For Strong Turbulence Compensation, Michael J. Steinbock
Implementation Of Branch-Point-Tolerant Wavefront Reconstructor For Strong Turbulence Compensation, Michael J. Steinbock
Theses and Dissertations
Branch points arise in optical transmissions due to strong atmospheric turbulence, long propagation paths, or a combination of both. Unfortunately, these conditions are very often present in desired operational scenarios for laser weapon systems, optical communication, and covert imaging, which suffer greatly when traditional adaptive optics systems either cannot sense branch points or implement non-optimal methods for sensing and correcting branch points. Previous research by Pellizzari presented a thorough analysis of various novel branch point tolerant reconstructors in the absence of noise. In this research a realistic model of the Air Force Institute of Technology's adaptive optics system is developed …
Multi-Observation Continuous Density Hidden Markov Models For Anomaly Detection In Full Motion Video, Matthew P. Ross
Multi-Observation Continuous Density Hidden Markov Models For Anomaly Detection In Full Motion Video, Matthew P. Ross
Theses and Dissertations
An increase in sensors on the battlefield produces an abundance of collected data that overwhelms the processing capability of the DoD. Automated Visual Surveillance (AVS) seeks to use machines to better exploit increased sensor data, such as by highlighting anomalies. In this thesis, we apply AVS to overhead Full Motion Video (FMV). We seek to automate the classification of soldiers in a simulated combat scenario into their agent types. To this end, we use Multi-Dimensional Continuous Density Hidden Markov Models (MOCDHMMs), a form of HMM which models a training dataset more precisely than simple HMMs. MOCDHMMs are theoretically developed but …
An Inquiry: Effectiveness Of The Complex Empirical Mode Decomposition Method, The Hilbert-Huang Transform, And The Fast-Fourier Transform For Analysis Of Dynamic Objects, Kristen L. Wallis
An Inquiry: Effectiveness Of The Complex Empirical Mode Decomposition Method, The Hilbert-Huang Transform, And The Fast-Fourier Transform For Analysis Of Dynamic Objects, Kristen L. Wallis
Theses and Dissertations
A review of current signal analysis tools show that new techniques are required for an enhanced fidelity or data integrity. Recently, the Hilbert-Huang transform (HHT) and its inherent property, the Empirical Mode Decomposition (EMD) technique, have been formerly investigated. The technique of Complex EMD (CEMD) was also explored. The scope of this work was to assess the CEMD technique as an innovative analysis tool. Subsequent to this, comparisons between applications of the Hilbert transform (HT) and the Fast-Fourier transform (FFT) were analyzed. MATLAB was implemented to model signal decomposition and the execution of mathematical transforms for generating results. The CEMD …
Low Cost, Low Complexity Sensor Design For Non-Cooperative Geolocation Via Received Signal Strength, Michael S. Butler
Low Cost, Low Complexity Sensor Design For Non-Cooperative Geolocation Via Received Signal Strength, Michael S. Butler
Theses and Dissertations
Obtaining accurate non-cooperative geolocation is vital for persistent surveillance of a hostile emitter. Current research for developing a small, cheap and energy efficient sensor network for non-cooperative geolocation measurements via received signal strength (RSS) is limited. Most existing work focuses on simulating a non-cooperative network (NN) and in doing so, simulated models often ignore localization errors caused from the hardware processing raw RSS data and often model environment-dependent errors as random. By comparing real-time measured non-cooperative geolocation data to a simulated system a more accurate model can be developed. This thesis discusses the development and performance of a small, low …
Dismount Threat Recognition Through Automatic Pose Identification, Andrew M. Freeman
Dismount Threat Recognition Through Automatic Pose Identification, Andrew M. Freeman
Theses and Dissertations
The U.S. military has an increased need to rapidly identify nonconventional adversaries. Dismount detection systems are being developed to provide more information on and identify any potential threats. Current work in this area utilizes multispectral imagery to exploit the spectral properties of exposed skin and clothing. These methods are useful in the location and tracking of dismounts, but they do not directly discern a dismount's level of threat. Analyzing the actions that precede hostile events yields information about how the event occurred and uncovers warning signs that are useful in the prediction and prevention of future events. A dismount's posturing, …
Ladar Range Image Interpolation Exploiting Pulse Width Expansion, Jeramy W. Motes
Ladar Range Image Interpolation Exploiting Pulse Width Expansion, Jeramy W. Motes
Theses and Dissertations
Laser Detection and Ranging (LADAR) systems produce both a range image and an intensity image by measuring the intensity of light reflected off a surface target. When the transmitted LADAR pulse strikes a sloped surface, the returned pulse is expanded temporally. This characteristic of the reflected laser pulse enables the possibility of estimating the gradient of a surface. This study estimates the gradient of the surface of an object from a modeled LADAR return pulse that includes accurate probabilistic noise models. The range and surface gradient estimations are incorporated into a novel interpolator that facilitates an effective three dimensional (3D) …
Binary Classification Of An Unknown Object Through Atmospheric Turbulence Using A Polarimetric Blind-Deconvolution Algorithm Augmented With Adaptive Degree Of Linear Polarization Priors, Mu J. Kim
Theses and Dissertations
This research develops an enhanced material-classification algorithm to discriminate between metals and dielectrics using passive polarimetric imagery degraded by atmospheric turbulence. To improve the performance of the existing technique for near-normal collection geometries, the proposed algorithm adaptively updates the degree of linear polarization (DoLP) priors as more information becomes available about the scene. Three adaptive approaches are presented. The higher-order super-Gaussian method fits the distribution of DoLP estimates with a sum of two super-Gaussian functions to update the priors. The Gaussian method computes the classification threshold value, from which the priors are updated, by fitting the distribution of DoLP estimates …
Local Histograms For Per-Pixel Classification, Melody R. Massar
Local Histograms For Per-Pixel Classification, Melody R. Massar
Theses and Dissertations
We introduce a rigorous mathematical theory for the analysis of local histograms, and study how they interact with textures that can be modeled as occlusions of simpler components. We first show how local histograms can be computed as a system of convolutions and discuss some basic local histogram properties. We then introduce a probabilistic, occlusion-based model for textures and formally demonstrate that local histogram transforms are natural tools for analyzing the textures produced by our model. Next, we characterize all nonlinear transforms which satisfy the three key properties of local histograms and consider the appropriateness of local histogram features in …
Spectral Detection Of Human Skin In Vis-Swir Hyperspectral Imagery Without Radiometric Calibration, Andrew P. Beisley
Spectral Detection Of Human Skin In Vis-Swir Hyperspectral Imagery Without Radiometric Calibration, Andrew P. Beisley
Theses and Dissertations
Many spectral detection algorithms require precise ground truth measurements that are hand-selected in the image to apply radiometric calibration, converting image pixels into estimated reflectance vectors. That process is impractical for mobile, real-time hyperspectral target detection systems, which cannot empirically derive a pixel-to-reflectance relationship from objects in the image. Implementing automatic target recognition on high-speed snapshot hyperspectral cameras requires the ability to spectrally detect targets without performing radiometric calibration. This thesis demonstrates human skin detection on hyperspectral data collected at a high frame rate without using calibration panels, even as the illumination in the scene changes. Compared to an established …
Ofdm-Based Signal Exploitation Using Quadrature Mirror Filter Bank (Qmfb) Processing, Felipe E. Garrido
Ofdm-Based Signal Exploitation Using Quadrature Mirror Filter Bank (Qmfb) Processing, Felipe E. Garrido
Theses and Dissertations
By performing QMFB processing with a given signal it is possible to obtain Frequency-Time (F-T) outputs that represent signal features such as bandwidth (W), center frequency (fc), signal duration (Ts), modulation type (AM, FM, BPSK, QAM, etc), frequency content and time allocation. Because of its unique structure, two widely used signals based on Orthogonal Frequency Division Multiplexing (OFDM) were chosen as signals of interest for demonstration. The general implementation of the QMFB process is described along with the basic structure of OFDM signals related to the physical layer perspective of 802.11a Wi-Fi and 802.16e WiMAX frame structures are described. The …
Passive Multistatic Radar Imaging Using An Ofdm Based Signal Of Opportunity, Matthew B. P. Rapson
Passive Multistatic Radar Imaging Using An Ofdm Based Signal Of Opportunity, Matthew B. P. Rapson
Theses and Dissertations
This paper demonstrates a proof of concept in using an OFDM-based signal of opportunity for SAR imaging purposes within a passive, multistatic radar construct. Two signal processing methods have been proposed to create phase history data. The same methods are applied in both a simulated software model and an experimental data collection environment to produce simulated SAR images using the CBP imaging algorithm. The images generated from both the experimental and simulated data were observed to be consistent with each other and with expectations in terms of resolution. Coherent addition of the images results in improved image resolution due to …
Simultaneous Range-Velocity Processing And Snr Analysis Of Afit's Random Noise Radar, T. Joel Thorson
Simultaneous Range-Velocity Processing And Snr Analysis Of Afit's Random Noise Radar, T. Joel Thorson
Theses and Dissertations
This paper presents two research objectives aimed at advancing the AFIT RNR signal processing algorithm and modeling capability toward the overarching goal of performing collision avoidance on an autonomous vehicle. In both research efforts, analytical, simulated, and measured results are provided and used to draw research conclusions. The first research effort is aimed at reducing the memory required for 2D processing in the time domain in order to distribute the processing algorithm across hundreds of processors on a GPU. Distributed processing reduces the overall 2D processing time and the feasibility of a near real-time implementation is studied. The second effort …