Georgia Tech has developed a new modeling and simulation tool that predicts both radar and electro-optical infrared (EO-IR) lateral range curves (LRCs) and sweep widths (SWs) under the Optimization of Radar and Electro-Optical Sensors (OREOS) program for US Coast Guard Search and Rescue (SAR) applications. In a search scenario when the location of the lost or overdue craft is unknown, the Coast Guard will conduct searches based upon standard procedure, personnel expertise, operational experience, and models. One metric for search planning is the sweep width, or integrated area under a LRC. Because a searching craft is equipped with radar and EO-IR sensor suites, the Coast Guard is interested in accurate predictions of sweep width for the particular search scenario. Here, we will discuss the physical models that make up the EO-IR portion of the OREOS code. First, Georgia Tech SIGnature (GTSIG) generates thermal signatures of search targets based upon the thermal and optical properties of the target and the environment; a renderer then calculates target contrast. Sensor information, atmospheric transmission, and the calculated target contrasts are input into NVESD models to generate probability of detection (PD) vs. slant range data. These PD vs. range values are then converted into LRCs by taking into account a continuous look search from a moving platform; sweep widths are then calculated. The OREOS tool differs from previous methods in that physical models are used to predict the LRCs and sweep widths at every step in the process, whereas heuristic methods were previously employed to generate final predictions.
Tactical platforms benefit greatly from the fusion of tracks from multiple sources in terms of increased situation awareness. As a necessary precursor to this track fusion, track-to-track association, or correlation, must first be performed. The related measurement-to-track fusion problem has been well studied with multiple hypothesis tracking and multiple frame assignment methods showing the most success. The track-to-track problem differs from this one in that measurements themselves are not available but rather track state update reports from the measuring sensors. Multiple hypothesis, multiple frame correlation systems have previously been considered; however, their practical implementation under the constraints imposed by tactical platforms is daunting. The situation is further exacerbated by the inconvenient nature of reports from legacy sensor systems on bandwidth- limited communications networks. In this paper, consideration is given to the special difficulties encountered when attempting the correlation of tracks from legacy sensors on tactical aircraft. Those difficulties include the following: covariance information from reporting sensors is frequently absent or incomplete; system latencies
can create temporal uncertainty in data; and computational processing is severely limited by hardware and architecture. Moreover, consideration is given to practical solutions for dealing with these problems in a multiple hypothesis correlator.
Georgia Tech been investigating method for the detection of covert personnel in traditionally difficult environments
(e.g., urban, caves). This program focuses on a detailed phenomenological analysis of human physiology and signatures
with the subsequent identification and characterization of potential observables. Both aspects are needed to support the
development of personnel detection and tracking algorithms. The difficult nature of these personnel-related problems
dictates a multimodal sensing approach. Human signature data of sufficient and accurate quality and quantity do not
exist, thus the development of an accurate signature model for a human is needed. This model should also simulate
various human activities to allow motion-based observables to be exploited. This paper will describe a multimodal
signature modeling approach that incorporates human physiological aspects, thermoregulation, and dynamics into the
signature calculation. This approach permits both passive and active signatures to be modeled. The focus of the current
effort involved the computation of signatures in urban environments. This paper will discuss the development of a
human motion model for use in simulating both electro-optical signatures and radar-based signatures. Video sequences
of humans in a simulated urban environment will also be presented; results using these sequences for personnel tracking
will be presented.
High resolution LiDAR data is used to augment spectral data to improve resolution/accuracy. Digital elevation
information, texture information, and spectral data are all combined into a single dataset and different clustering
algorithms are used on the raster information and compared with clusters of spectral data alone. Long term goals
of the work are to find efficient and effective methods of combining different data sets of varying resolution from
different sources into a single dataset for analysis to improve data and classification resolution and accuracy.
Human motion in visual and long-wave infrared video imagery is investigated. A simple moving target
tracker is used to segment out the subject of interest in a video sequence. Pixel level changes of the subject's
size and position within the image are then used to form a pair of signals. Standard techniques in signal
processing are then applied to find features of interest. The long term goals of the work are to find a means
for associating tracks of humans in optical video data with poorly resolved micro-Doppler RF signals, and to
Often in hyperspectral overhead land mine imagery, there exists clutter with similar spatial and spectral characteristics
to those of land mines. However groups of clutter features are rarely related spatially in the same way
that groups of mines are related. For this reason, recognition of field patterns in overhead land mine imagery
is critical to the detection of mine fields. The material presented here addresses means by which to spatially
sample overhead hyperspectral imagery for the accentuation of mine field patterns. Our initial approach is to
assume that the mines are laid out in a particular field pattern. We then search for spectral anomalies that
are spatially distributed according to such a pattern. For this purpose, we utilize an RX detector with locally
estimated mean and covariance matrix. We then use the pattern to predict the locations of additional mines.
These locations provide us with search regions for the use of a second anomaly detector, in this case we use an
anomaly detector based upon an eigenspace separation transform. Examples are provided using LWIR imagery.
Mine fields are often distinguishable in overhead hyperspectral LWIR imagery due to the spatial pattern in
which the mines are laid. Recognition of these field patterns in overhead landmine imagery shows promise for
enhancing the ability to detect mine fields. However, before one can search for a field pattern in an image, it is
necessary to determine the orientation and size of the pattern within the image, should it exist. We present a
method for determining likely scales and orientation for grids of landmines. The approach is to consider pairs
of interest points and then look for patterns in the slopes of the lines connecting them. The dominant slope
then determines an orientation angle. Next, we look for patterns in the distances between pairs of points that
have a slope close to the orientation angle. An application to detecting mine fields via recognition of patterns of
features in hyperspectral LWIR imagery is given.