The MITRE Corporation recently hosted the first Netted Sensors Community Workshop in McLean, Virginia, on 24
October - 26 October 2005. The Workshop was sponsored by the Defense Advanced Research Projects Agency
(DARPA), Office of the Secretary of Defense (OSD) Director of Defense Research and Engineering (DDR&E), and the
National Science Foundation (NSF). The goal was to establish and sustain an annual Netted Sensors workshop that
brings together Government, Industry and Academia to accelerate the development and transition of appropriate Netted
Sensor technologies to solve real world problems. The workshop provided a forum focused on the application of netted
sensing research and development (R&D) activities to solve existing and future Department of Defense (DoD),
Intelligence Community (IC), Department of Homeland Security (DHS), and Environmental sensing problems. The
Netted Sensors workshop brought together the Science and Technology (S&T) community, Industry, and Government /
Military organizations to (1) share, discuss and disseminate new R&D results, (2) highlight new commercial products
and technologies, and (3) identify and discuss nationally important sensing problems suitable for Netted Sensing
solutions. This paper provides a summary of the presentations that were made at the workshop as well as
recommendations for future workshops.
This paper presents a new technique for FOPEN SAR (foliage penetration synthetic aperture radar) image formation of Ultra Wideband UHF radar data. Planar Subarray Processing (PSAP) has successfully demonstrated the capability of forming multi- resolution images for X and Ka band radar systems under MITRE IR&D and the DARPA IBC program. We have extended the PSAP algorithm to provide the capability to form strip map, multi- resolution images for Ultra Wideband UHF radar systems. The PSAP processing can accommodate very large SAR integration angles and the resulting very large range migration. It can also accommodate long coherent integration times and wide swath coverage. Major PSAP algorithm features include: multiple SAR sub-arrays providing different look angles at the same image area that can enable man-made target responses to be distinguished from other targets and clutter by their angle dependent specular characteristics, the capability to provide a full resolution image in these and other selected areas without the processing penalty of full resolution in non required areas, and the capability to include angle-dependent motion compensation within the image formation process.
Hidden Markov models (HMMs) are probabilistic finite state machines that can be used to represent random discrete time data. HMMs produce data through the use of one or more `observable' random processes. An additional `hidden' Markov process controls, which of the `observable' random processes is used to produce an individual data observation. Helicopter radar signatures can be represented as quasi- periodic 1D discrete time series that can be analyzed using HMMs. In the HMM helicopter detection and classification algorithm developed in this study, the states of the `hidden portion' of the HMM were used to represent time dependence alignments between the radar and helicopter rotor structures. For example, the times when specular reflections occur were used to define a `blade-fish' state. Since blade- flash frequency, and the corresponding non-blade-flash state duration, is an important feature in helicopter detection and classification. HMMs that allowed direct specification of state duration probabilities were used in this study. The HMM approach was evaluated using X-Band radar data from military helicopters recorded at Ft. A.P. Hill. After initial adaptive clutter suppression and blade-flash enhancement preprocessing, a set of approximately 1,000 raw in-phase and quadrature data records were analyzed using the HMM approach. A correct target classification rate that varied between 98% for a PRF of 10 KHz to 91% at a 2.5 KHz PRF was achieved.
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