A canonical problem for autonomy is search and discovery. Often, searching needs to be unpredictable in order
to be effective. In this paper, we investigate and compare the effectiveness of the traditional and predictable
lawnmower search strategy to that of a random search. Specifically the family of searches with paths determined
by heavy tailed distributions called Lévy stable searches is investigated. These searches are characterized by long
flight paths, followed by a new random direction, with the flight path lengths determined by the distribution
parameter α. Two basic search scenarios are considered in this study: stationary targets, and moving targets,
both on planar surfaces. Monte-Carlo simulations demonstrate the advantages of Lévy over the lawnmower
strategy especially for moving targets. Ultimately to corroborate the suitability of the Lévy strategy for UAVs,
we implement and demonstrate the feasibility of the algorithm in the Multiple Unified Simulation Environment
(MUSE), which includes vehicle's constraints and dynamics. The MUSE / Air Force Synthetic Environment for
Reconnaissance and Surveillance (AFSERS) simulation system is the primary virtual ISR and UAV simulation
within DOD for command and staff level training for the Joint Services.
Automatic target recognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify
y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ∈ Rnxm(n<<m) and x is a sparse vector whose non-zero entries identify the input y. The signal x will be sparse with respect to the dictionary A as long as y is a valid target. The algorithm can reject clutter and background, which are part of the input image. The detection and recognition problems are solved by finding the sparse-solution to the undetermined system y=Ax via Orthogonal Matching Pursuit (OMP) and l1 minimization techniques.
Visible and MWIR imagery collected by the Army Night Vision and Electronic Sensors Directorate (NVESD) was
utilized to test the algorithm. Results show an average detection and recognition rates above 95% for targets at ranges
up to 3Km for both image modalities.
A multi-resolution, parallel approach to retinal blood vessel detection has been introduced that can also be used as a
discriminant for fovea detection. Localized adaptive thresholding and a multi-resolution, multi-window Radon
transform (RT) are utilized to detect the retinal vascular system. Multi-window parameter transforms are intrinsically
parallel and offer increased performance over conventional transforms. Large vessels are extracted in low-resolution
mode, whereas minor vessels are extracted in high-resolution mode further increasing computational efficiency. The
image is adaptively thresholded and then the multi-window RT is applied at the different resolution levels. Results from
each level are combined and morphologically processed to improve final performance.
A systematic approach has been implemented to perform fovea detection. The algorithm relies on a probabilistic
method to perform initial segmentation. The intensity image is re-mapped into probability space to detect areas with
low-probability of occurrence. Intensity and probability information are coupled to produce a binary image that
contains potential fovea candidates. The candidates are discriminated based upon their location within the blood vessel
Hyperspectral image analysis is an important component of advanced hyperspectral image understanding. We present a new approach that identifies unique materials and the abundance of these materials in a hyperspectral image. This approach uses physical constraints on material abundances and reflectances, and avoids the presence of a dark material class by parameterizing pixel illumination. The results are optimally generated in both supervised and unsupervised modes. Applications of the image analysis approach are also presented.
The problem of predicting HRR radar and SAR signal magnitudes based on a limited number of observations is a challenging component of feature aided tracking. In this paper we describe the application of a scattering-based tomographic technique that builds persistent scatterer models of ground vehicles from a collection of HRR and/or SAR observations from varying look angles. Results are obtained using MSTAR data. Target detection results are shown using ROC curves and compared with nearest observation matching. Application of these techniques to the move-stop-move problem of vehicle tracking is also described.