A need exists for a self-forming, self-organizing, cognitive, cooperative, automated unmanned aerial vehicle (UAV) network system to more efficiently perform UAV-based maritime search and rescue (SAR) operations. Although current search patterns (e.g., traditional “lawn mower” methods) are thorough, they result in too much time spent searching lowprobability areas. This decreases the chances of a successful rescue and increases the risk of lost recovery opportunities (e.g., death due to hypothermia in the case of human search targets). Our goal is to optimize UAV-based SAR operations. As directed by an onboard computer, UAVs would fly coordinated search patterns based on the target’s last known position and the direction and speed of winds and currents. By enabling the UAVs to act collectively and cooperatively, we can enhance the efficiency and effectiveness of a multi-UAV network’s SAR mission. To achieve this, we applied cooperative game theory as an enabling function in the development of a cognitive system encompassing multiple vehicles. Based on simulations, we showed that an optimal dynamic search pattern and vehicle positioning strategy can be realized using decision algorithms based on elements of game theory.
In modern warfare concepts, the use of wireless communications and network-centric topologies with unmanned aerial vehicles (UAVs) creates an opportunity to combine the familiar concepts of wireless beamforming in opportunistic random arrays and swarm UAVs. Similar in concept to the collaborative beamforming used in ground-based randomly distributed array systems, our novel approach improves wireless beamforming performance by leveraging cooperative location and function knowledge. This enables the capabilities of individual UAVs to be enhanced, using swarming and cooperative beamforming techniques, for more-effective support of complex radar jamming and deception missions. In addition, a dedicated System Oversight function can be used to optimize the number of beamforming UAVs required to jam a given target and manage deception assets.
The capability of a software algorithm to automatically align same-patient dental bitewing and panoramic x-rays over time is complicated by differences in collection perspectives. We successfully used image correlation with an affine transform for each pixel to discover common image borders, followed by a non-linear homography perspective adjustment to closely align the images. However, significant improvements in image registration could be realized if images were collected from the same perspective, thus facilitating change analysis. The perspective differences due to current dental image collection devices are so significant that straightforward change analysis is not possible. To address this, a new custom dental tray could be used to provide the standard reference needed for consistent positioning of a patient’s mouth. Similar to sports mouth guards, the dental tray could be fabricated in standard sizes from plastic and use integrated electronics that have been miniaturized. In addition, the x-ray source needs to be consistently positioned in order to collect images with similar angles and scales. Solving this pose correction is similar to solving for collection angle in aerial imagery for change detection. A standard collection system would provide a method for consistent source positioning using real-time sensor position feedback from a digital x-ray image reference. Automated, robotic sensor positioning could replace manual adjustments. Given an image set from a standard collection, a disparity map between images can be created using parallax from overlapping viewpoints to enable change detection. This perspective data can be rectified and used to create a three-dimensional dental model reconstruction.
Ocean floor mapping using video is a method to simply and cost-effectively record large areas of the seafloor. Obtaining visual and elevation models has noteworthy applications in search and recovery missions. Hazards to navigation are abundant and pose a significant threat to the safety, effectiveness, and speed of naval operations and commercial vessels. This project’s objective was to develop a workflow to automatically extract metadata from marine video and create image optical and elevation surface mosaics. Three developments made this possible. First, optical character recognition (OCR) by means of two-dimensional correlation, using a known character set, allowed for the capture of metadata from image files. Second, exploiting the image metadata (i.e., latitude, longitude, heading, camera angle, and depth readings) allowed for the determination of location and orientation of the image frame in mosaic. Image registration improved the accuracy of mosaicking. Finally, overlapping data allowed us to determine height information. A disparity map was created using the parallax from overlapping viewpoints of a given area and the relative height data was utilized to create a three-dimensional, textured elevation map.
KEYWORDS: Signal detection, Doppler effect, Signal to noise ratio, Signal processing, Receivers, Sensors, Mobile communications, Matrices, Telecommunications, Databases
Dynamic Spectrum Access (DSA) is widely seen as a solution to the problem of limited spectrum, because of its ability
to adapt the operating frequency of a radio. Mobile Ad Hoc Networks (MANETs) can extend high-capacity mobile
communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high
potential impact, cognitive radio employs spectrum sensing to facilitate the identification of allocated frequencies not
currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic
location, while secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not
using it. We populate a spatial radio environment map (REM) database with known information that can be leveraged in
an ad hoc network to facilitate fair path use of the DSA-discovered links. Utilization of high-resolution geospatial data
layers in RF propagation analysis is directly applicable. Random matrix theory (RMT) is useful in simulating network
layer usage in nodes by a Wishart adjacency matrix. We use the Dijkstra algorithm for discovering ad hoc network node
connection patterns. We present a method for analysts to dynamically allocate node-node path and link resources using
fair division. User allocation of limited resources as a function of time must be dynamic and based on system fairness
policies. The context of fair means that first available request for an asset is not envied as long as it is not yet allocated or
tasked in order to prevent cycling of the system. This solution may also save money by offering a Pareto efficient
repeatable process. We use a water fill queue algorithm to include Shapley value marginal contributions for allocation.
We discuss a robust method for quantifying change of multi-temporal remote sensing point data in the presence of affine registration errors. Three dimensional image processing algorithms can be used to extract and model an electronic module, consisting of a self-contained assembly of electronic components and circuitry, using an ultrasound scanning sensor. Mutual information (MI) is an effective measure of change. We propose a multi-resolution 3D fractal algorithm which is a novel extension to MI or regional mutual information (RMI). Our method is called fractal mutual information (FMI). This extension efficiently takes neighborhood fractal patterns of corresponding voxels (3D pixels) into account. The goal of this system is to quantify the change in a module due to tampering and provide a method for quantitative and qualitative change detection and analysis.
A novel approach using a support vector machine (SVM) is proposed to classify bare earth points in LiDAR point clouds. Using graph based segmentation, the LiDAR point cloud is segmented into a set of topological components. Several features establishing relationships from those components to their neighboring components are formulated. The SVM is then trained on the segment features to establish a model for the classification of bare earth and non bare earth points. Quantitative results are presented for training and testing the proposed SVM classifier on the ISPRS data set. Using the ISPRS data set as a training set, qualitative results are presented by testing the proposed SVM classifier on data downloaded from Open Topography; which covers a variety of different landscapes and building structures in Frazier Park, California. Despite the data being captured from different sensors, and collected from scenes with different terrain types and building structures, the results shown were processed with no parameter changes. Furthermore, a confidence value is returned indicating how well the unforeseen data fits the SVM’s trained model for bare earth recognition.
KEYWORDS: General packet radio service, 3D modeling, Reflection, Environmental sensing, Data modeling, 3D acquisition, Data processing, Sensors, Process modeling, Computer architecture
We discuss a robust method for optimal oil probe path planning inspired by medical imaging. Horizontal wells require
three-dimensional steering made possible by the rotary steerable capabilities of the system, which allows the hole to
intersect multiple target shale gas zones. Horizontal "legs" can be over a mile long; the longer the exposure length, the
more oil and natural gas is drained and the faster it can flow. More oil and natural gas can be produced with fewer wells
and less surface disturbance. Horizontal drilling can help producers tap oil and natural gas deposits under surface areas
where a vertical well cannot be drilled, such as under developed or environmentally sensitive areas. Drilling creates well
paths which have multiple twists and turns to try to hit multiple accumulations from a single well location. Our
algorithm can be used to augment current state of the art methods. Our goal is to obtain a 3D path with nodes describing
the optimal route to the destination. This algorithm works with BIG data and saves cost in planning for probe insertion.
Our solution may be able to help increase the energy extracted vs. input energy.
KEYWORDS: Signal detection, Doppler effect, Detection and tracking algorithms, Sensors, Signal to noise ratio, Signal processing, Mobile communications, Databases, Telecommunications, Interference (communication)
Dynamic Spectrum Access, which through its ability to adapt the operating frequency of a radio, is widely believed to be
a solution to the limited spectrum problem. Mobile Ad Hoc Networks (MANETs) can extend high capacity mobile
communications over large areas where fixed and tethered-mobile systems are not available. In one use case with high
potential impact cognitive radio employs spectrum sensing to facilitate identification of allocated frequencies not
currently accessed by their primary users. Primary users own the rights to radiate at a specific frequency and geographic
location, secondary users opportunistically attempt to radiate at a specific frequency when the primary user is not using
it. We quantify optimal signal detection in map based cognitive radio networks with multiple rapidly varying phase
changes and multiple orthogonal signals. Doppler shift occurs due to reflection, scattering, and rapid vehicle movement.
Path propagation as well as vehicle movement produces either constructive or destructive interference with the incident
wave. Our signal detection algorithms can assist the Doppler spread compensation algorithm by deciding how many
phase changes in signals are present in a selected band of interest. Additionally we can populate a spatial radio
environment map (REM) database with known information that can be leveraged in an ad hoc network to facilitate
Dynamic Spectrum Access. We show how topography can help predict the impact of multi-paths on phase change, as
well as about the prediction from dense traffic areas. Utilization of high resolution geospatial data layers in RF
propagation analysis is directly applicable.
We introduce a novel application for biometric data analysis. This technology can be used as part of a unique and systematic approach designed to augment existing processing chains. Our system provides image quality control and analysis capabilities. We show how analysis and efficient visualization are used as part of an automated process. The goal of this system is to provide a unified platform for the analysis of biometric images that reduce manual effort and increase the likelihood of a match being brought to an examiner’s attention from either a manual or lights-out application. We discuss the functionality of FeatureSCOPE™ which provides an efficient tool for feature analysis and quality control of biometric extracted features. Biometric databases must be checked for accuracy for a large volume of data attributes. Our solution accelerates review of features by a factor of up to 100 times. Review of qualitative results and cost reduction is shown by using efficient parallel visual review for quality control. Our process automatically sorts and filters features for examination, and packs these into a condensed view. An analyst can then rapidly page through screens of features and flag and annotate outliers as necessary.
We present a fractal feature space for 3D point watermarking to
make geospatial systems more secure. By exploiting the self
similar nature of fractals, hidden information can be spatially
embedded in point cloud data in an acceptable manner as
described within this paper. Our method utilizes a blind scheme
which provides automatic retrieval of the watermark payload
without the need of the original cover data. Our method for
locating similar patterns and encoding information in LiDAR
point cloud data is accomplished through a look-up table or
code book. The watermark is then merged into the point cloud
data itself resulting in low distortion effects. With current
advancements in computing technologies, such as GPGPUs,
fractal processing is now applicable for processing of big data
which is present in geospatial as well as other systems. This
watermarking technique described within this paper can be
important for systems where point data is handled by numerous
aerial collectors including analysts use for systems such as a
National LiDAR Data Layer.
We present an innovative way to autonomously classify LiDAR points into bare earth, building, vegetation, and other
categories. One desirable product of LiDAR data is the automatic classification of the points in the scene. Our algorithm
automatically classifies scene points using Compressed Sensing Methods via Orthogonal Matching Pursuit algorithms
utilizing a generalized K-Means clustering algorithm to extract buildings and foliage from a Digital Surface Models
(DSM). This technology reduces manual editing while being cost effective for large scale automated global scene
modeling. Quantitative analyses are provided using Receiver Operating Characteristics (ROC) curves to show
Probability of Detection and False Alarm of buildings vs. vegetation classification. Histograms are shown with sample
size metrics. Our inpainting algorithms then fill the voids where buildings and vegetation were removed, utilizing
Computational Fluid Dynamics (CFD) techniques and Partial Differential Equations (PDE) to create an accurate Digital
Terrain Model (DTM) [6]. Inpainting preserves building height contour consistency and edge sharpness of identified
inpainted regions. Qualitative results illustrate other benefits such as Terrain Inpainting's unique ability to minimize or
eliminate undesirable terrain data artifacts.
The most commonly used smoothing algorithms for complex data processing are low pass filters. Unfortunately, an
undesired side effect of the aforementioned techniques is the blurring of scene discontinuities in the interferogram. For
Digital Surface Map (DSM) extraction and subsidence measurement, the smoothing of the scene discontinuities can
cause inaccuracy in the final product. Our goal is to perform spatially non-uniform smoothing to overcome the
aforementioned disadvantages. We achieve this by using an Anisotropic Non-Linear Diffuser (ANDI). Here, in this
paper we will show the utility of ANDI filtering on simulated and actual Interferometric Synthetic Aperture Radar
(IFSAR) data for preprocessing, subsidence measurement and DSM extraction to overcome the difficulties of typical
filters. We also compare the results of the ANDI filter with a wavelet filter. Finally, we detail some of our results of the
New Orleans IFSAR research project with Canadian Space Agency, NASA, and USGS. The Harris LiteSiteTM Urban
3D Modeling software is used to illustrate some of the results of our RADARSAT-1 processing.
High resolution Digital Surface Models (DSMs) may contain voids (missing data) due to the data collection process used
to obtain the DSM, inclement weather conditions, low returns, system errors/malfunctions for various collection
platforms, and other factors. DSM voids are also created during bare earth processing where culture and vegetation
features have been extracted. The Harris LiteSiteTM Toolkit handles these void regions in DSMs via two novel
techniques. We use both partial differential equations (PDEs) and exemplar based inpainting techniques to accurately
fill voids. The PDE technique has its origin in fluid dynamics and heat equations (a particular subset of partial
differential equations). The exemplar technique has its origin in texture analysis and image processing. Each technique
is optimally suited for different input conditions. The PDE technique works better where the area to be void filled does
not have disproportionately high frequency data in the neighborhood of the boundary of the void. Conversely, the
exemplar based technique is better suited for high frequency areas. Both are autonomous with respect to detecting and
repairing void regions. We describe a cohesive autonomous solution that dynamically selects the best technique as each
void is being repaired.
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