Experimental Adaptive Cylindrical Array
Randy L. Haupt
University of Nevada
Electrical Engineering Department/260
Reno, NV 89557-0153
702-784-6927
hauut@ee.unr.edu
Hugh Southall
ARINC Incorporated
Lexington, MA 01731
781-981-5499
hsouthall@ll.mit.edu
Ab$@uct-- An adaptive antenna places nulls in its antenna
pattern in the direction of the interfering sources.
Adaptive antennas are a subset of smart antennas, and
have received considerable interest in the recent wireless
revolution. This paper presents a successful application of
a genetic algorithm with an experimental antenna for
placing a null in the direction of an interference source.
The experiment took place at the Air Force Research
Laboratory (AEXL)/Sensors Directorate, Hanscom AFB,
MA. Results show that a null can be placed down to the
noise floor of the measurement system within 20 to 50
power measurements. Thus, the approach to adaptive
nulling is a viable means of quickly placing a null in the
sidelobes of a phased array antenna.
TABLEOF C o m m s
1. INTRODUCTION
2. ADAPI~VENULLINGALGOWIHM
3. EWERIMENTALC~~VDR~CALARRAY
4. Experimental Results
5. CONCLUSIONS
I. INTRODUCTION
The frequency specis getting more and more
crowded and the potential for systems interfering with
each other is growing. An alternative to help systems
survive a strong interference environment is to use an
adaptive antenna to receive desirable signals. Adaptive
antenna technology started in the 1950s and has steadily
progressed since then. Serious drawbacks to current
adaptive algorithms include the adaptive a l g o r i h .
s
Require an expensive receiver at each element makes array impractical to build.
Get stuck in a local minimum - doesn't use full
potential of the antenna to reject interference.
Slowly converge - often not useful for radar or
scanning applications.
Can't be implemented on existing antennas-they
require adjustable ampfitude weights and receivers at
every element in addition to beam steering phase
shifters.
Cause the main beam to move from its desired
pointing direction.
Significantly raise the sidelobe levels of a low
sidelobe array.
Most approaches to adaptive antennas fall into four
categories. The lirst category requires a receiver at each
array element [l]. This approach forms a correlation
matrix from the signals at each element and inverts the
matrix to fmd the optimum weights that maximize signal
to signal plus interference ratio. Many variations to this
approach exist, but they all require knowledge of the
signal received at each element. Multiple receivers are
expensive, and the receivers must be continuously
calibrated, therefore this approach is impractical for large
arrays. Other limitations result from noise in the
correlation measurements and difficulties performing the
matrix inversion.
A second category of nulling assumes the interference
location is known and the characteristics of the attenuators
and/or phase shifters are accurately predictable. Given this
information, weights that form the desired null(s) are
mathematically derived by the computer and implemented
on the m y . Such an approach is impractical because the
location of the interfering signal is not likely to be known.
Also, the array weights have tolerances and errors that
prevent their exact specification [2].
A third category of adaptive nulling randomly guesses at
possible amplitude and phase settings for the antenna
array. This Monte Carlo approach to adaptive nulling is
he-consuming and usually not practical to implement for
real-time systems [3].
A final approach implements a numerical optimization
algorithm to minimize the total output power of the array.
This approach tqpically forms a gradient vector by
perturbing the phase and/or amplitude settings of the
0-1234-5678-0/99/$5.000 1999 IEEE
291
incident
elements to find the weights that minimize the total output
power [4]. These approaches are slow and get stuck in
local minima. This type of gradient optimization can be
used with phase and amplitude control as well but has
been shown to be less effective through than the genetic
algorithm [5].
The approach presented in this paper uses a genetic
algorithm to find the phase and amplitude weights that
minimize the total output power of the array. We assume
that the desired signal enters the mainbeam and interfering
signals enter the sidelobes. The genetic algorithm is
implemented on a PC that controls the eight element
cylindrical array described in the next section.
field
antennaelements
\ m
k
phase
shifters
attenuators
2. ADAPTIVE NLJLLINGALGORITHM
A genetic algorithm is a computer program that finds an
optimum solution by simulating genetics and natural
selection. In this application the phase shifter settings
evolve until the antenna pattern has a null in the direction
of the jammer [6]. A genetic algorithm was chosen for this
application, because it is a very efficient method for
searching an extremely large, discrete space of phase and
amplitude settings for the minimum array output power.
An adaptive, phase-only array has 2" possible phase and
attenuator settings (N = number of elements and P =
number of attenuator and phase shifter hits used for
nulling), many corresponding to local minima in the total
power output. Such a large number of settings (and local
minima) make random search and gradient based
algorithms impractical to use. An adaptive phase-only
genetic algorithm applied to a computer model of a linear
array has been reported by one of the authors 171. This
work extends the previous work to amplitude and phase
nulling on an experimental cylindrical antenna array.
An adaptive algorithm modifies the quantized phase and
amplitude weights based on the total output power of the
array. The goal of the adaptive array is to minimize total
output power, which consists of the interference signal
and possibly the desired signal. If no interference were
present, the algorithm would minimize the desired signal.
We solved this dilemma by using a limited number of
digital phase shifter and attenuator bits. Using the lower
order bits for nulling allows formation of nulls in the
sidelobes without significant impact on the main beam
(desired signal). Figure 1 shows a model of the adaptive
antenna array. The computer generates an amplitude and
phase setting and sends the setting to the array. Next, the
output power associated with that setting is read and
stored in the computer with the adapted weights. Recall
that the least significant hits are used for nulling, so the
main beam will not be nulled, and the desired signal will
receive minimal attenuation. This algorithm is suitable to
use when the mainkam is steered from one angle to the
next, because the genetic algorithm just continually
optimizes the amplitude and phase seitings.
292
Figure 1.Diagram of an adaptive linear array.
Fill phase and
attenuation settings
matrix with random
I
*
Figure 2. Flowchart of an adaptive genetic algorithm.
Figure 2 shows a flowchart of the adaptive
amplituddphase genetic algorithm. It begins with an
initial population consisting of a maaix filled with random
ones and zeros. Each row of the m a t h (chromosome)
consists of the nulling bits for each element placed sideby-side. There are NP columns and M rows. The output
power corresponding to each chromosome in the matrix is
measured and placed-ina vector (Figure 3). M is relatively
small (between 12 and 20 worked fine for us). The output
power and corresponding chromosomes are ranked from
lowest output power to highest output power. Next, the
bottom 50% of the chromosomes is discarded, because
they have the greatest output power. The algorithm
generates new chromosomes to replace the bottom 50%
discarded (Figure 4). The top 50% of the chromosomes
are mated pair-wise, i.e. one to two, three to four, etc. This
produces the same number of children as parents and is
sufficient to replenish the discarded bottom 50% of the
chromosomes. A random point is selected and bits to the
right of the random point are swapped to form two new
chromosomes. These new chromosomes are placed in the
mamx to replace two settings that were discarded, and
their output powers are measured. When enough new
chromosomes are created to replace those discarded, the
chromosomes are ranked and the process repeated. A
small number (less than 1%) of the nulling bits in the
matrix are mutated - randomly switched from a one to
zero or vise versa. These mutations &ow the algorithm to
try new areas of the search space while still converging on
a solution. We do not alter the best phase setting. More
general descriptions of genetic algorithms can be found in
[SI and [gl.
phase and amplitude
nulling bits
output
phase shifter and
attenuator settings
v :
gf
1000
phase shifter and
attenuator settings
output
power
010 010 000 001 00
001 000 110 010 001
000010001010001
001001000001100
-0010
.ooool~wowl~M)
--
Figure 4. Two parents are selected from the population to
generate two new offspring.
3. EXPERIMENTAL
C~INJJRICAL
ARRAY
The experimental phased array antenna was developed by
Air Force Research Laboratory (ME)at Hanscom AFB,
MA for experiments with artificial intelligence techniques,
such as neural networks and genetic algorithms [lo]. The
antenna consists of 128 vertical columns (16 dipoles per
column) equally spaced around a cylinder that is 41 inches
in diameter (Figure 5 shows a sector of the cylinder). The
outputs of the 16 dipoles are combined to form a fixed
elevation beam with a peak gain 3" above horizontal. Only
eight columns of elements are active in this application.
The eight-element sector is 22.5O in arc (1116 of the
cylinder), with the eight elements spaced about one inch,
or 0.42h, apart at the center frequency of 5 GHz.
w
0101
0101
100
2
Figure 3. The adaptive nulling bits calculated by the
genetic algorithm are sent to the adaptive array and the
power output is measured for each setting.
Figure 5. The cylindrical array has eight active elements
that cover a span of 22.5 degrees. This figure shows a
sector of the cylinder.
293
A unique feature of this antenna is that all the active
elements can be connected to the power combiner at once
(i.e. a standard corporate-fed phased array) or one element
at a time can be connected to the receiver, while the others
are terminated in a matched load. The latter mode
simulates a digital beamforming (DBF) antenna. In this
application, we had all eight elements connected in a
corporate feed. Thus, the adaptive element only had
access to the total received power of the array and not the
amplitude and phase of the signals at each of the elements.
The output of each element is connected to a single
channel containing an eight-bit phase shifter and eight-bit
attenuator. All eight channels are combined in a power
combiner as described above, and this output goes to a
phasdamplitude receiver. The attenuators are linear over
an 80 dB range with the least significant bit having an
attenuation of ,3125 dB. The antenna must be calibrated
in order to form a main beam. In this case, the calibration,
or quiescent, pattern is a 25 dB ;=3 Taylor taper. Phase
shifters are adjusted to compensate for the curvature of the
array and unequal path lengths through the feed.
superimposed on the quiescent pattern. The null at 45' is
-56 dB and about 31 dB below the quiescent pattern.
Since this null is below the noise floor of the measurement
system, the algorithm cannot improve any further. Figure
8 shows the convergence of the genetic algorithm for a
population size of 16 chromosomes. Note that the
algorithm converged in only two iterations, or less than 24
power measurements. No time measurements are reported,
because the experimental system is orders of magnitude
slower than an operational system. The solid line is the
null depth of the best chromosome and the dashed line is
the average null depth for the entire population (16
chromosomes). In this case, the average plot is important,
because the antenna constantly receives signals, so a low
average power is important for improving the SNlR.Only
one chromosome each generation is mutated (mutation
rate of 0.1 %).
-10-
Figure 6 shows the antenna mounted in the anechoic
chamber at AFRL.The chamber is 72 ft. x 36 ft. x 36 ft. A
horn antenna serves as the feed and is located 47 ft. from
the antenna. The source is CW and the phasdamplitude
receiver is a SA 1780. Measurements have a dynamic
range of about 50 dB.Thus, nulls that are 50 dB or more
below the peak of the main beam are the best we could
hope for. Phase shifters and attenuators are controlled
with a HT Basic program from a PC.
-quiescent
adapted
I
I
I
-50
0
angle in degrees
50
Figure 7.A null was placed in the antenna pattern at 45'.
-25
population average
1
"At \
Figure 6. Photograph of the antenna inside the anechoic
chamber.
4.A
E
L
-
-600
ResULTS
The januner was a CW source at 5 GHz. Only the four
least significant bits of both the 8-bit phase shifters and
attenuators were used. Figure 7 shows the adapted pattern
294
-55
.-
\
2
--._____
4
6
iteration
8
10
12
Figure 8. Graph shows the convergence of the genetic
algorithm when the interference was at 45".
The adapted pattern has a large sidelobe at -45" in
addition to putting a null at 4 5 " . This phenomenon is
characteristic of phase-only nulling. We used amplitude
weighting in this experiment, but the effects of the
amplitude weights were so small that they can be ignored.
Theory predicts that the increase in the symmemc
sidelobe should be ahout 3 dB. Figure 8 shows an increase
of approximately 14 dB. The sidelobes on either side of
the adaptive null also increased. Figure 9 shows the
adapted pattern for a null at 28.5' superimposed on the
quiescent pattern. The null depth is -49.4 dB, or 22 dB
below the quiescent pattern. Figure 10 shows the
convergence of the genetic algorithm for a population size
of 16 chromosomes. The null was formed in four
iterations or 40 power measurements. The solid line is the
null depth of the best chromosome and the dashed line is
the average null depth for the entire population (16
chromosomes). The average sidelobe level for the 16
chromosomes of the final population is -34.8 dB. The
adapted pattern raised the sidelohe at -28.5" by
approximately 10 dB.The sidelobe at 75' increased about
18 dB.
-50;
Y
1
2
iteration
3
4
Figure 10. Graph shows the convergence of the genetic
algorithm when the interference was at 28.5".
Several possibilities for futnre exploration include:
Place two sources to use as jammers. The algorithm
can be tested for sources entering two sidelobes or the
mainbeam and one sidelobe.
Modify the genetic algorithm so several of the
parameters can he readily changed: mutation rate,
size of population, number of bits used for nulling,
and different types of nulling (phase-only, amplitudeonly, and phase and amplitude). These parameters
greatly impact the convergence of the algorithm.
Improve the crude genetic algorithm implemented in
this experiment
-501
I
I
-50
0
angle in degrees
50
Figure 9. A null was placed in the antenna pattern at
28.5'.
5. CONCLUSIONS
The genetic algorithm quickly placed nulls in the array
antenna pattern without resorting to a receiver at each
array element. Limiting the nulling controls to a few least
significant bits of the phase sbifters and attenuators
allowed for better control of the antenna pattern as well as
improving algorithm convergence.
Use more attenuator bits for this application. Since
the attenuators are calibrated in dB, the use of only
four bits in effect made the adaptive algorithm phaseonly.
Investigate the frequency dependence of the nulls.
This can be done with a CW source by freezing the
may weights and varying the frequency of the CW
source.
Extend the results to a planar array.
REFERENCES
[l]R. T. Compton, Jr., Adaptive Antennas Concepts and
Performance,Englewood Cliffs, NJ:Prentice Hall, 1988.
[21 H. Steyskal, "Simple method for pattern nulling by
phase perturbation," IEEE AP-S Trans., Vol. 31, No. 1,
295
pp. 163-166, Jan 83.
[3] R. A. Monzingo and T. W. Miller, Introduction to
Adaptive Antennas, New York Wiley, 1980.
[4] R.L. Haupt, "Adaptive nulling in monopulse
antennas," IEEE AP-S Transactions, Vol. AP-36, No. 1,
Jan 88.
[SI Y. Chung and R.L. Haupt, "Amplitude and phase
adaptive nulling with a genetic algorithm," 1998
IEEENRSI International Symposium, Atlanta, GA, Jun
98.
[6] J. H. Holland, "Genetic algorithms," Sci. h e r . , pp
66-72, July 1992,
[7] R. L. Haupt, "Phase-only adaptive nulling with genetic
algorithms,"IEEE AP-S Trans., vol. 45, May 91.
[8] Southall, H.L., Simmers, J.A., and ODonnell, T.H.,
"Direction finding in phased arrays with a neural network
beamformer," IEEE AP-S Trans., Vol. 43, No. 12, Dec
97, pp. 1369-1374.
[91 Southall, H.L., Santarelli, S.,and Martin, E., "Neural
network heam-steering for phased array Applications
Symposium, Robert Allerton Park, Univ. of IL,
Champaign-Urbana, E,Sep 95.
[lo] R.L. Haupt and Sue Ellen Haupt, Practical Genetic
Algorithms, New York John Wiley & Sons, 1998.
296
Randy L Haupt is Professor and
Chair of Electrical Engineering at
the University of Nevada at Reno.
He recently retired from the USAF
as a Lieutenant Colonel and
Professor of Electrical Engineering
at the USAF Academy, CO. His
work experience includes elecm'cal
engineer for the OTH-B Radar
Program OfFce and an antenna research engineer for
Rome Air Development Center, both at Hanscom AFB,
MA. He received his Ph.D. from the University of
Michigan, his MSEEfrom Northeastern University, MS in
Engineering Management for Western New England
College, and BSEEfrom the USAF Academy.
Hugh L. Southall graduated from
the University of Texas at Arlington
with a BS in electrical engineering in
1968 and from Texas Tech. university
with an MSEP- and PhD in electrical
engineering in 1970 and 197S,
respectively. His is a retired US Air
Force officer. His current research
area is antenna systems. He is
employed at ARINC? Incorporated, at
the Air Force Office at MIT Lincoln Laboratory,
Hanscom AFB, Massachusetts. He is also a part-time
leciurer at Northeastern University. Southall is an IEEE
Senior Member, and a member of Tau Beta Pi and Eta
Kappa Nu.