Direction of Arrival Estimation (DOA) in Interference & Multipath
Transcript of Direction of Arrival Estimation (DOA) in Interference & Multipath
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Direction of Arrival Estimation (DOA)
in Interference & Multipath Propagation
Dr. H. Howard Fan
GIRD Systems, Inc.
310 Terrace Ave.
Cincinnati, Ohio 45220
This presentation is based on publicly available information only, some copyright
restrictions may apply, not for commercial use.
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Scope
• There are many location methods
Source location vs. Self-location (Navigation)
Active vs. Passive
Network based (GPS) vs. Single platform (DOA or AOA)
• We will concentrate on source location with a single
platform equipped with a sensor array, passive
• Emphasis on DOA estimation
• Narrowband signals only
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
• Smoothing Method
• Source Association and Locating the Sources
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
Smoothing Methods
• Source Association and Locating the Sources
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Objective of DOA Estimation
• To find DOA (relative to the array orientation) of all
incident RF rays
• Could have multipath propagation and interference
DOA Est /
Tracking
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Narrowband Signal Sources
• A complex sinusoid
( ) j j t j ts t e e e
• A real sinusoid is a sum of two sinusoids
1 2cos( )2 2
j j t j j t j t j tt e e e e e e
• A delay of a sinusoid is a phase shift
0 0
0( ) ( )j t j tj ts t t e e e s t
• Apply approximately to narrowband signals
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A Uniform Linear Array
1( ) ( )x t s t• If the received signal at sensor 1 is
• Then the received signal at sensor i is
0( 1) sin
1( ) ( ) ( ) ( )i i
i dj
j j cix t e x t e s t e s t
• Then it is delayed at sensor i by 0( 1) sini
i d
c
0A signal source
“impinges” on the array
with an angle
c: propagation speed
( ) j ts t e
0
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
Smoothing Methods
• Source Association and Locating the Sources
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Beamforming
• Delay-and-sum type
• Reverse the delay/phase on each sensor to “line-up”
the received signal phase
• Adding all phase-shifted sensor outputs to enhance the
received SNR in that direction
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Beamforming
• By adjusting the delay or phase shifts, we electronically steer the
beam through all look directions to find DOA of an incident signal
• Received signal at the ith sensor:
0( 1) sin
1( ) ( ) ( ) ( )i i
i dj
j j cix t e x t e s t e s t
• is the incidence angle of the received signal
• The delay or phase shift of the beamformer on the ith sensor is computed according to a “look angle” to get the output:
0
0
( 1) sin
*
1 1
( 1) [sin sin ]
1
( ) ( ) ( )
( )
i dN N jc
i i i
i i
i dN jc
i
y t w x t e x t
e s t
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Beamforming
• If , then the output becomes
0
0
0
( 1) [sin sin ]
1 1
( ) ( ) ( ) ( )
i dN Njc
i i
y t e s t s t Ns t
• I.e., the output is enhanced N times
• Since noise is not correlated, noise is not enhanced
• So SNR is enhanced N times
• At other steering angles, the complex weights may cancel so the output is not enhanced or even degraded
• This is the designed purpose of a beamformer
0
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Beamforming
• Beam pattern: A gain pattern as a function of the steering angle
0( 1) [sin sin ]
0
1
( , )
i dN jc
i
G e
0 0 o
0 30
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Beamforming
• Sensor spacing is usually wavelength/2
• Larger spacing creates “grating lobes” that confuse with
the main lobe
• Smaller spacing
reduces the total
aperture – lower
spatial resolution
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Beamforming
• Number of sensors (array aperture) are directly related to
spatial resolution
4-element (aperture = 1.5 wavelength) 7-element (aperture = 3 wavelength)
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Beamforming
• Problem 1: A wide mainlobe causes poor spatial
resolution Two targets 10 degrees apart Cannot distinguish them
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Beamforming
• Problem 2: A strong interferer can come into a sidelobe
and completely swamp weak signal in the look direction
Beamformer output:
• Control sidelobes by Dolph-Chebyshev shading, w. limited effect
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Pros/Cons of Beamforming
• Can find only one DOA at a time
• Spatial resolution determined by number of sensors in
an array, but generally not very good unless having a
large number of sensors
• Works for other array shapes also, need to know
sensor positions in an array
• Sensitive to sensor position, gain, and phase errors,
must calibrate carefully to make it work well
• Interference is a big problem
• Multipath is a much lesser problem
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
Smoothing Method
• Source Association and Locating the Sources
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MUSIC
• Able to find DOAs of multiple sources in “one-shot”
• High spatial resolution compared with beamforming
methods (I.e., a few antennas can result in very high
spatial resolution)
• MUSIC stands for Multiple Signal Classifier
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Narrowband Signal Sources
• Consider I narrowband signal sources
1 2
1 1 2 2( ) , ( ) , , ( ) Ij t j t j t
I Is t e s t e s t e
• Assume that all amplitudes are uncorrelated
2;{ }
0;
i
i j
i jE
i j
• Recall received signal on the ith sensor:
0( 1) sin
1( ) ( ) ( ) ( )i i
i dj
j j cix t e x t e s t e s t
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Signal Model
• Put received signals at all N sensors together:
• is called a “steering vector”
sin1
22 sin
3
( 1) sin
1
( )
( )
( ) ( ) ( ) ( ) ( )
( )
dj
c
dj
c
N dN jc
x tex t
t x t s t s te
x t
e
x a
( )a
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Signal Model
• If there are I source signals received by the array, we get
a “signal model”:
x(t) --- received signal vector ( N by 1 )
s(t) --- source signal vector ( I by 1 )
n(t) --- noise vector ( N by 1 )
( N by I )
1( ) [ ( ), , ( )]T
It s t s ts
• Sources are independent, noises are uncorrelated
• Column of A can also be normalized
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The MUSIC Algorithm
• Compute the N x N correlation matrix
sR
2 2
1{ ( ) ( )} .{ , , }H
IE t t diag sR s s
2
0{ ( ) ( )}H HE t t x sR x x AR A I
where
• If the sources are somewhat correlated so is not
diagonal, it will still work if has full rank.
• If the sources are correlated such that is rank deficient,
then it is a problem. A common solution is “spatial
smoothing”.
Q: Why is the rank of (being I) so important?
A: It defines the dimension of the signal subspace.
sR
sR
sR
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The MUSIC Algorithm
• For N > I, the matrix is singular, i.e., H
sAR A
2 ; 1,2, ,i i i i N xR u u
• But this implies that is an eigenvalue of
• Since the dimension of the null space of is N-
I, there are N-I such eigenvalues of
• Since is non-negative definite, there are I other
eigenvalues such that
• Let be the ith eigenvector of corresponding to
2 2
0 0i
2
0
2
0
2
i
2
0det[ ] det[ ] 0H s xAR A R I
xR
H
sAR A
xR
iuxR
2
i
xR
2 2 2 2
0 0, 1, , ; , 1, ,i ii I i I N
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The MUSIC Algorithm
• This implies
• Partition the N-dimensional vector space into the
signal subspace and the noise subspace sU nU
2 2
0[ ] ; 1,2, ,H
i i i i i N x sR u AR A I u u
2 2
0( ) ; 1,2, ,H
i i i i N sAR A u u
2 2
0( ) ; 1,2, ,
0; 1, ,
H i i
i
i I
i I N
s
uAR A u
2 2 2
0
1 1
: , : ,
[ ]
i i
I I N
i I i I
s n
s n
U U
U U u u u u
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The MUSIC Algorithm
• The steering vector is in the signal subspace
• Signal subspace is orthogonal to noise subspace
sU
nU
( )ia
(1) means I linear combinations of columns of A equal the signal subspace spanned by columns of
(2) means the linear combinations of columns of A, i.e., the signal subspace, is orthogonal to
2 2
0( ) ; 1,2, , (1)
0; 1, , (2)
H i i
i
i I
i I N
s
uAR A u
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The MUSIC Algorithm
• The steering vector is in the signal subspace
• Signal subspace is orthogonal to noise subspace
• This implies that
• So the MUSIC algorithm searches through all angles , and plots the “spatial spectrum”
( )H
i na U 0
( )ia
1( )
( )HP
na U
• Wherever exhibits a peak
• Peak detection will give spatial angles of all incident sources
, ( )i P
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The MUSIC Algorithm
1. Compute the signal correlation matrix
2. Perform SVD/EVD on and separate the smallest eigenvalues from larger eigenvalues
3. Eigenvectors corresponding to the smallest eigenvalues form a noise subspace
4. Search through all angles in the MUSIC spatial spectrum
5. Peaks correspond to DOAs
1( )
( )HP
na U
xR
xR
nU
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The MUSIC Algorithm
MUSIC spatial spectrum compared with other methods
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Pros/Cons of The MUSIC Algorithm
• Can find multiple DOAs with high resolution
• Number of sensors must be more than number of sources
• Works for other array shapes also, need to know sensor positions in an array
• Very sensitive to sensor position, gain, and phase errors, need careful calibration to make it work well
• Searching through all could be computationally expensive
• Interference is not much a problem, just another source whose DOA can be found with other sources
• Multipath is a big problem
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
Smoothing Methods
• Source Association and Locating the Source
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Adaptive Beamforming
• Rather than fixed weights/phase, adapt the weights according to the signal environment
• The generalized sidelobe canceller (GSC) achieves this w. flexible constraints
• Delay-and-sum beamformer output y(t):
0( ) ( ) ( ) ( ) ( ) ( )H Hy t t s t a x a a
• The GSC output y(t):
0( ) ( ) ( ) ( )H Hy t t s t w x w a
w is a vector of complex weights, more general
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Adaptive Beamforming
• Q: How do we choose the weights w?
• A: By some constrained optimization
• Constraints:
1) Array gain at the look direction should be preserved
When
2) Array gain at some other directions may need to be zero
This is also called null-steering
1 1( ) ( ) 1H H w a a w
2 2( ) ( ) 0H H w a a w
0( ) ( ) ( ) ( )H Hy t t s t w x w a
1 0 we have, ( ) ( )y t s t
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Adaptive Beamforming
• Jointly, we write these constraints as
1
1 2
2
1( )( ) ( )
0( )
HH H
H
aw a a w C w g
a
• More constraints, other constraints, can be imposed
• The cost function is:
2{| ( ) | } { ( ) ( )}H H HE y t E t t xw x x w w R w
• The optimization problem is:
min subject to H H x
w
w R w C w g
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Adaptive Beamforming
• The solution to this optimization problem is:
• Since we minimize the output energy, a strong interferer will result in a deep notch in its direction
ab ab
1 1 1( )H x xw R C C R C g
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Adaptive Beamforming
• Another interpretation: Decompose w into
G I w w Bw
is the orthogonal compliment of : H B C C B 0
B
Gw
Iw
Iw
( )tx( )y t
• Upper branch: Main channel; Lower branch: Auxiliary channel
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Adaptive Beamforming
• I.e., allows gain (null) in the desired directions – main channel
• Since , the matrix B is a “blocking matrix” that blocks the signal (null) in the desired directions – auxiliary channel
• is designed to produce a replica of interference leaking into the main channel to subtract it out
1( ) so that H H
G G
w C C C g C w g
Gw
H C B 0
Iw
1( )H H
I G
x xw B R B B R w
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Adaptive Beamforming
• Advantages of adaptive beamforming
– Able to reduce effect of interferences from unknown angles
– Can steer look direction and multiple null directions
– Get a signal copy easily
• Shortcomings of adaptive beamforming
– Spatial resolution is still low
– Need enough weights (antenna channels) to obtain enough degrees of freedom: At least # of constraints + 1
– Sidelobe levels may not be controlled perfectly
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
Smoothing Methods
• Source Association and Locating the Sources
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Multipath Propagation Issue
• Urban areas have a lot of multipaths
• Beamforming DOA algorithms are not affected by
this much
• MUSIC fails in multipath!
• This is because if there is multipath, two or more
DOAs will be from the same source – i.e., some
sources in MUSIC signal model are correlated
• Now with I DOAs there are less than I sources
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Multipath Propagation Issue
• Correlated sources disable MUSIC!
• E.g.:
• This is called rank deficiency
– The rank of will be less than I
– The signal subspace has dimension less than I
– There are less than I peaks in the MUSIC spectrum
– Which of the I DOAs will give less than I peaks?
• In fact all peaks will be wrong.
1 1( ) [ ( ), ( )]Tt s t as ts2 2
1 1
2 2 2
1 1
{ ( ) ( )}H aE t t
a a
sR s s Rank 1
H
sAR A
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Multipath Propagation Issue
• Remedy: Spatial Smoothing, still find I DOAs
– L overlapping subarrays
– M sensors in each subarray, M > I
– N total sensors
– N = M + L − 1
. . . . . . . . .1 2 L M M+1 N
• Let be the received signal vector of the ith subarray ( )i tx
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Multipath Propagation Issue
• One signal case:
sin1
22 sin
3
( 1) sin
1
( )
( )
( ) ( ) ( ) ( ) ( )
( )
dj
c
dj
c
N dN jc
x tex t
t x t s t s te
x t
e
x a
sin
1 2( ) ( ) ( ), ( ) ( ) ( ), etc.d
jc
M Mt s t t e s t
x a x a
• In general ( 1) sin
( ) ( ) ( )i d
jc
i Mt e s t
x a
( 1) sin
( ) ( ) ( )i d
jc
i Mt e s t
x a
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Multipath Propagation Issue
• With I DOAs:
1
1
( 1) sin ( 1) sin
1 1
( 1) sin
1
( 1) sin
( ) ( ) ( ) ( ) ( )
( ) ( ) ( )
I
IM
i
i d i dj j
c ci M M I I
i dj
c
M M I
i dj
c
t e s t e s t
e
t
e
A
D
x a a
O
a a s
O
( ) ( )i M it tx A D s (Noise has been ignored so far)
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Multipath Propagation Issue
• Compute correlation matrix of each subarray
2
0{ ( ) ( )}H H H
i i i M i i ME t t x sR x x A D R D A I
• Now average the correlation matrices of all subarrays
2
0
1 1
1 1{ ( ) ( )}
L LH H H
L i i M i i M
i i
E t tL L
x sR x x A D R D A I
• Note the dimension of is M by I
• The dimension of the matrix in the brackets is I by I
MA
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Multipath Propagation Issue
• The matrix in the brackets has full rank I if L is large
enough, i.e.,
• Provided M > I, there is a noise subspace in
• Now can apply MUSIC to
• Will detect I DOAs with this spatial smoothing
• Price paid is that more sensors are required: M > I,
M + L = N L more sensors to “de-correlate”
LxR
2
0
1 1
1 1{ ( ) ( )}
L LH H H
L i i M i i M
i i
E t tL L
x sR x x A D R D A I
LxR
L I
L I
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Outline
• Basics of DOA Estimation
Beamforming Type
High Resolution Type
• Adaptive Beamforming DOA Estimation in Strong
Interference
• High Resolution DOA Estimation in Multipath
Smoothing Methods
• Source Association and Locating the Sources
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Source Association Under Multipath
• Several paths belong to one source, multiple sources
• Can detect all DOAs of multiple paths and multiple
sources
• But how many sources are there?
• Need to know which DOAs can be associated to which
sources – source association
• Then locate the sources in several ways
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Source Association Under Multipath
• Once sources are associated with DOAs
– Locate the sources by using multipath if we know the
reflection geometry – back tracing
• Multipath works
to our advantage
in this case
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Source Association Under Multipath
• Alg. works for any DOA method, easier with MUSIC
1. Compute the received signal correlation matrix based
on all sensors, as in MUSIC but un-smoothed
2. Compute the noise subspace matrix whose rank is
the number of sources J, not the number of paths I
3. Based on the estimated DOAs, compute the overall
steering matrix A
4. Find minimum eigenvalues of and their
corresponding eigenvectors , the number of these
eigenvalues is the number of sources J
5. Construct I by J matrix
nU
H H
n nA U U A
iq
1[ ]JQ q q
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Source Association Under Multipath
6. Find J groups of rows of
that are independent among the groups but dependent
within each group, these correspond to groups of paths
associated with each source
• Dependency test can be done by, e.g., dividing the
corresponding elements in two rows and compute the
variance of the ratios
1[ ]JQ q q
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Source Association Under Multipath
• E.g.: 15 antennas in a ULA, two sources, 5 paths
– DOA from Source 1:
– DOA from Source 2:
– We only know 5 DOAs rank ordered as:
o o30 and 60 o o o5 , 25 , and 55
o o o o o60 , 30 , 5 , 25 , 55
• Put them into A in this order
• Computed eigenvalues of which are
{20.0559, 17.9585, 14.8226, 0.0430, 0.0088}
• Obviously I = 5 but J = 2
H H
n nA U U A
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Source Association Under Multipath
• Dependency test on
the rows of Q
1 & 2 belong to one
3, 4, 5 belong to the
other
1 2
0.4152 + j0.4824 0.2656 + j0.1121
0.4062 + j0.4989 0.2913 + j0.1122
[ ] -0.2290 + j0.0746 0.3954 - j0.3430
-0.2429 + j0.0498 0.3883 - j0.3502
-0.2210 + j0.1123 0.2770 - j0.4418
Q q q
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Source Association Under Multipath
• When reflection geometry is unknown
– Identify the LOS paths from the reflected paths if either the
source or the receiver is moving
• Once LOS paths are identified, the sources can be more
easily located by
– Ray back tracing
– Several different angles of LOS DOA to triangulate
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Source Association Under Multipath
• Stationary receiver: All paths have fixed DOAs;
• Moving receiver: Paths have varying DOAs, DOA
variations differ between LOS path and reflected paths.
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Source Association Under Multipath
• All DOAs need to
be tracked
• Reflected paths may
be eliminated over
time by choosing
the longest
continuous path as
the LOS path
• Most likely, reflected paths appear and disappear
intermittently as one travels, but not the LOS paths
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Source Association Under Multipath
• Caution: If not tracked properly, two DOAs may be
erroneously identified after they cross
• Special measures are needed to prevent this
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Conclusions
• Beamforming (adaptive)
– Can reduce effect of interferences from unknown DOAs
– Can steer look direction and multiple null directions
– Spatial resolution is low
– Resilient to multipath propagation
• MUSIC (with smoothing)
– Can find multiple DOAs with high resolution
– Very sensitive to sensor position, gain, and phase errors,
need careful calibration to make it work well
– Spatial smoothing is difficult to achieve on other than ULA
• Source association can be applied to both methods
• Source location done by ray back trace/triangulation
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About GIRD Systems, Inc.
• Founded in 2000 - based in Cincinnati
• Specializes in communications and
signal processing, especially
developing novel algorithms to solve
challenging problems
• As of Jan. 2010, won more than 15
Phase I awards and 7 Phase II awards
from Navy, Air Force, Army
• Partnerships with many large
contractors including Northrop
Grumman, L-3 Communications, etc.
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About GIRD Systems, Inc.
• Key Technology Areas
– Interference Mitigation (no reference, in-band)
– Direction Finding (wideband, high-resolution)
– Location/Navigation (Assisted GPS, GPS denied,
signals of opportunity)
– Wireless Network Security (physical layer)
– Power Amplifier Linearization
– Novel Communications systems/modeling
• Contact Information: GIRD Systems, Inc. Phone: (513) 281-2900
310 Terrace Ave. Fax: (513) 281-3494
Cincinnati, OH 45220 E-mail: [email protected]
USA Web: www.girdsystems.com