This document describes a challenge problem whose scope is two-fold. The first aspect is to develop SAR CCD
algorithms that are applicable for X-band SAR imagery collected in an urban environment. The second aspect relates to
effective data compression of these complex SAR images, where quality SAR CCD is the metric of performance.
A set of X-band SAR imagery is being provided to support this development. To focus research onto specific areas of
interest to AFRL, a number of challenge problems are defined.
The data provided is complex SAR imagery from an AFRL airborne X-band SAR sensor. Some key features of this data
set are: 10 repeat passes, single phase center, and single polarization (HH). In the scene observed, there are multiple
buildings, vehicles, and trees. Note that the imagery has been coherently aligned to a single reference.
Computer network security has become a very serious concern of commercial, industrial, and military organizations
due to the increasing number of network threats such as outsider intrusions and insider covert activities.
An important security element of course is network intrusion detection which is a difficult real world problem
that has been addressed through many different solution attempts. Using an artificial immune system has been
shown to be one of the most promising results. By enhancing jREMISA, a multi-objective evolutionary algorithm
inspired artificial immune system, with a secondary defense layer; we produce improved accuracy of intrusion
classification and a flexibility in responsiveness. This responsiveness can be leveraged to provide a much more
powerful and accurate system, through the use of increased processing time and dedicated hardware which has
the flexibility of being located out of band.
This document describes a challenge problem whose scope is the detection, geolocation, tracking
and ID of moving vehicles from a set of X-band SAR data collected in an urban environment. The
purpose of releasing this Gotcha GMTI Data Set is to provide the community with X-band SAR data
that supports the development of new algorithms for SAR-based GMTI. To focus research onto
specific areas of interest to AFRL, a number of challenge problems are defined.
The data set provided is phase history from an AFRL airborne X-band SAR sensor. Some key
features of this data set are two-pass, three phase center, one-foot range resolution, and one
polarization (HH). In the scene observed, multiple vehicles are driving on roads near buildings.
Ground truth is provided for one of the vehicles.