DTIC ADA521836: Impulsive Noise Suppression via Adaptive Filtering

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by  Defense Technical Information Center

REMOTE SENSING
Impulsive Noise Suppression via Adaptive
Noise Modeling: Atmospheric noise, power lines,
Filtering
and sporadic local interferers such as engine noise are
all types of noise sources that are not well modeled by
G.S. San Antonio
Gaussian probability distributions. Noise generated
Radar Division
by these sources is inherently transient in nature (e.g.,
a single car driving near the array, a single arc from
Introduction: Wide area surveillance is a common
a power line transformer, or a lighting strike from
task for many of the nation’s defense agencies. Cover-
a distant thunder storm). To this end, a probability
age needs to be persistent 24 hours a day and through
distribution describing such random processes should
varying interference environments. High Frequency
possess “heavy tails” to accurately express the probabil-
Over-the-Horizon Radar (HF-OTHR) is capable of
ity of an extreme valued random event. The drawback
providing wide area coverage that is on the order of
of modeling using heavy tailed distributions is that
hundreds of thousands of square kilometers. The
detector design can quickly become intractable due to
challenges of using OTHR are manifold. Operating in
an often crowded and noisy frequency spectrum (3–30
MHz) coupled with the complex and ever-changing
state of the ionosphere presents difficulties higher-
frequency radars do not face. In this article we describe
a technique for suppressing impulsive noise-like
disturbances that often corrupt OTHR data. This new
technique is enabled by recent advances in high-speed
computing that allow near-real-time implementation
of sophisticated adaptive signal processing algorithms.
Background: OTHR is a surveillance radar tech-
nology that both the U.S. Navy and U.S. Air Force have
used over the past forty years because of its wide area
coverage capability and low operating cost.1 Research-
ers at the Naval Research Laboratory (NRL) developed
one of the first OTHR systems in 1961 called MADRE.
Currently NRL is helping to develop advanced OTHR
systems while also serving as technical advisors to
the Navy’s AN/TPS-71 Relocatable OTHR (ROTHR)
program which consists of three radar sites providing
surveillance coverage of the Caribbean Sea, Central
America, and South America. Figure 11 shows a FIGURE 11
AN/TPS–71 ROTHR receive array.
ROTHR receive antenna array and Figure 12 shows the
approximate coverage area of the three radars.
OTHR operates by either refracting signals off complex expressions for probability density functions
layers of the ionosphere (skywave) or propagating a (PDFs). An alternative to using heavy tailed distribu-
surface wave over the conductive ocean that follows tions is to allow a degree of non-stationarity. Simply
the curvature of the Earth. Among the many sources put, non-stationarity means that the moments or
of performance limitation, impulsive noise is a major statistics of a random process change (usually with time
contributor. Impulsive noise differs from normal or space if we are considering a space-time random
noise sources like thermal noise and cosmic back- process).
ground noise in that it is highly transient with very In the extreme, non-stationarity could be taken
large amplitude peaks. Traditional Gaussian statistical to mean that the random process has different statis-
assumptions fail to properly model this type of noise, tics everywhere. It is exactly this interpretation that
and therefore normal target detection algorithms may allows us to hypothesize a space-time point process
perform poorly, often with very high false-alarm rates. model for the noise field present in OTHR data. The
Recognizing that current data models were inadequate, full data model is composed of three independent
a signal-plus-noise model was developed that can be parts: backscattered energy from targets, background/
used to accurately estimate when data has been cor- thermal Gaussian noise, and other noise that appears
rupted by impulsive noise and subsequently filter the like impulsive events. The OTHR data is modeled as
data so as to remove the impulsive noise. the combination of two space-time point processes:
228 2009 NRL REVIEW
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Impulsive Noise Suppression via Adaptive Filtering
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Prescribed by ANSI Std Z39-18
REMOTE SENSING
VA SITE
SW SITE
BERMUDA
ATLANTIC OCEAN
MEXICO
CUBA DOMINICAN
REPUBLIC
BELIZE PUERTO
GUATEMALA HONDURAS JAMAICA HAITI SE SITE RICO
EL SALVADOR NICARAGUA
COSTA RICA
PANAMA VENEZUELA
SURINAM
FRENCH
PACIFIC OCEAN COLUMBIA GUIANA
ECUADOR
BRAZIL
PERU
STATUTE MILES
0 200 400 600 800
NAUTICAL MILES
0 200 400 600 800 BOLIVIA
FIGURE 12
Conceptual illustration of OTHR operational environment.
the first models targets and ground clutter (point-like known to be difficult and often analytically intractable.
in range-Doppler domain) and the second models Only recently with the advent of highly parallelized
impulsive noise (point-like in inverse range-Doppler computer architectures have such problems been
domain, that is, the fast time–slow time domain before solvable in near real time. Our solution to the ML
range-Doppler processing). Given this hypothesized estimation problem makes use of a well know statisti-
data model, modern statistical estimation methods can cal algorithm called the expectation maximization
be used to adaptively filter the impulsive noise. (EM) algorithm. Using this iterative algorithm, an ML
estimate may be found. Construction of an appropriate
Adaptive Noise Filtering: Adaptive signal process- filtering matrix is based on finding the minimum mean
ing describes a method of processing data in such a way squared error (MMSE) estimate of the impulsive noise
that important information is inferred from data in a free data. Figure 13 shows the results of applying this
real-time manner and is allowed to influence the way algorithm to a set of simulated OTHR data.
in which data is processed. More reliable and robust An alternative to the model-based maximum-like-
performance is achieved at the expense of slightly lihood approach we have described is to systematically
diminished peak performance. The adaptive impulsive identify data samples corrupted by impulsive noise
noise filtering algorithm that has been developed works and remove these samples from the data. Simulations
by first estimating the statistics of target and noise and analysis of real OTHR data revealed that the ML
random processes jointly, and then constructing a approach performed better than several methods previ-
filtering matrix to recover an estimate of the received ously developed for HF-OTHR data based on extrac-
data with the impulsive noise removed. tion and replacement.2 Additionally, these extraction
The estimation step requires finding the joint and replacement techniques are highly dependent on
maximum likelihood target range-Doppler power spec- the severity of the impulsive noise corruption and the
trum and noise time-pulse power spectrum estimates. method used to replace the extracted samples (usually
Maximum likelihood (ML) estimation problems are linear prediction).
2009 NRL REVIEW 229
REMOTE SENSING
Conclusion: Non-cooperative interference will
always be a problem for surveillance sensor systems. It
is possible, however, to minimize its impact on system
performance given the correct signal processing. In this
article a new technique for solving the impulsive noise
suppression problem was presented. In the future,
researchers at NRL will continue to push the limits of
OTHR technology by developing new and innovative
signal processing solutions.
Acknowledgment: This work was supported by the
Missile Defense Agency.
[Sponsored by AFRL/Air Fund]
References
1 J.F. Thomason, “Development of Over-the-Horizon Radar in
the United States,” in Proceedings of the International Radar
Conference, Adelaide, Aust., Sept. 3–5 2003, pp. 599–601.
2 J.R. Barnum and E.E. Simpson, “OTH Radar Sensitivity
Improvement by Temporal Excision of Impulsive Noise,” Rome
Laboratory, Griffiss Air Force Base, NY, Technical Report RLTR
94-169, Sept. 1994, contract F30602-93-C-0112.
Range-Doppler Domain - No Processing Range-Doppler Domain - With Processing
40 40
10 10
35 Target 35
ell20 30 ell 20 30
C C
e 25dB e 25dB
g g
n30 n 30
Ra 20 Ra 20
40 15 40 15
10 10
50 50
-20 -10 0 10 20 -20 -10 0 10 20
Doppler Frequency (Hz) Doppler Frequency (Hz)
(a) (b)
Time-Domain Data - No Processing Time-Domain Data - With Processing
50 24
5 5 23.5
45
c) c)
e e 23
ms 40 ms
e ( 10 dB e ( 10 22.5dB
m 35 m
Ti Impulse Noise Ti
st st 22
Fa 15 30 Fa 15
21.5
25
21
20 20
0 5 10 15 20 0 5 10 15 20
Slow Time (Pulse Number) Slow Time (Pulse Number)
(c) (d)
FIGURE 13
Example of adaptive noise suppression technique operating on simulated data. (a) Range-Doppler image with
no noise suppression processing; (b) time-domain representation of data, with impulsive noise present; (c) Range-
Doppler image after impulsive noise suppression, target can be detected; (d) time-domain data after impulsive noise
has been filtered.
230 2009 NRL REVIEW