Aerospace and Electronic Systems Magazine September 2017 - 42

Expert System CFAR

Figure 2.

Increased power-aperture product is not always feasible.

increased radar aperture or dramatically higher average transmit
power (light-heartedly described with the graphic in Figure 2).
The simple goal was to look for immediate gains in filtering, false
alarm control, and track processing. At that point, management and
leadership were open to all suggestions. Mr. Fred Demma, Chief
of Surveillance Technology at RADC, heavily promoted a philosophy of "MASS for MIPS" in which we emphasized algorithm development and computing solutions over power-aperture, all while
remaining cognizant of the limitations due to the thermal noise
floor. An investigation into target correlation on extended coherent
dwell ensued [2].
Multiple researchers responded to Dr. Schneible's call for technology solutions to this challenging AMTI problem, including
members of the aforementioned LAD research team. Dr. Brown
focused on improving the linear dynamic range of the radar receiver and improving the balance of the in-phase and quadrature
baseband signals. This proved extremely beneficial for realizing
enhanced coherent processing. This research motivated Dr. Brown
to develop novel IF sampling techniques using out-of-band noise
injection that aided in developing the Expert System CFAR. Mr.
van Etten studied the application of modern spectral estimators to
ADI, specifically focusing on autoregressive techniques to address
the problem of weak signal suppression [3]. Mr. Thomas Maggio,
also from RADC, was instrumental in applying a coherent form
of the noisy area tracker, a noncoherent track technique put into
production using analog phosphor storage in the 1960s. Mr. Maggio investigated techniques for analyzing postdetection declarations, separating false alarms from desired signals via "real-time"
statistical methods. At that same time, Mr. Robert Ogrodnik from
RADC sponsored Mr. Alan Corbeil, Dr. John DiDomizio, and Mr.
Lee Moyer, all from the Technology Service Corporation, to investigate track before detect (TBD) [4], a knowledge-aided, fully
coherent, and much improved form of the aforementioned noisy
area tracker.
Dr. Schneible assigned the task of trade space analysis between
and among these and other signal and data processing techniques
to the first author. Initial results indicated that an improved CFAR
detector would yield a cost effective and immediate benefit to airborne wide area surveillance radar, as the adaptive threshold multiplier could be systematically reduced, weaker returns detected,
and false alarms precluded with improved exploitation of clutter
statistics. An Expert System CFAR would increase computational
complexity only modestly, and computing was a cost driver in the
42

mid-1980s. We pursued concept development, systems engineering integration, and theoretical (statistical) analysis of the emerging Expert System CFAR in addition to research in space-time
adaptive processing (STAP) for clutter rejection in airborne radar
[5]. STAP is a form of multidimensional spatial-temporal filtering
that potentially improves detection performance in airborne early
warning and ground moving target indication (GMTI) radar.
Fortuitously, Dr. Northup Fowler, a software expert and senior
research scientist at RADC, was independently pursuing intelligence, surveillance, and reconnaissance applications of innovative
techniques in advanced computing. Dr. Fowler pursued, among
other software technology, a form of programming that emulates
the knowledge and analytic skills of a human expert. Furthermore,
basic and early applied research funds from the office of the Chief
Scientist became available to scientists and engineers conducting
research in this area, especially in speech and radar signal processing, in order to apply expert reasoning software to enhance the
real-world performance of mathematical algorithms. Our research
in Expert System CFAR detection processing received additional
support from the office of the Chief Scientist.
The research of Dr. Hermann Rohling [6] further motivated
the first author to pursue a more rigorous systems-oriented design
of the Expert System CFAR detector. We took several months to
study the problem and propose a detector with the potential for
performance improvements but with limited demands for high-end
computing. In that era, real-time embedded parallel processing
was experimental in nature, extremely costly, and oriented towards
space applications [7]. We proposed a technology solution that
required very little advanced computing hardware. This solution,
which has since impacted several fielded systems, was designed
for simplicity of implementation. We proposed to run several different (albeit well characterized) CFAR detector algorithms in
parallel, and to fuse these detector results using conventional algorithms. The menu of standard CFAR detector algorithms included
cell averaging (CA), GO, TM, ordered statistic (OS), and smallest
of (SO). Additionally, several advanced CFAR detector algorithms
developed by Mr. James Sawyers from the Hughes Aircraft Company (now Raytheon) were included. We leveraged conventional
fusion and track processing algorithm technology developed independently by colleagues Dr. Pramod Varshney from Syracuse
University and Dr. Yaakov Bar-Shalom from the University of
Connecticut. These techniques were used to integrate the various
CFAR detector decisions and to produce a global declaration of
target present or target absent.
We fully understood that fusion algorithms were all developed
under the assumption that each data input to the decision processor was independent of all others, and this was clearly not true in
Expert System CFAR. In the problem under analysis, identical data
is processed using various CFAR detectors, and a global decision
made using the aforementioned fusion rules. Still, we explored this
algorithmic approach experimentally, and developed the technology more fully as performance proved worthy. Initial demonstrations in the Surveillance Laboratory at RADC were astonishing! In
clutter and electromagnetic interference limited environments, the
earliest Expert System CFAR outperformed all other CFAR detectors. At that same time, Dr. Vincent Vannicola from RADC began

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