Note: Descriptions are shown in the official language in which they were submitted.
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HANDHELD SYNTHETIC ANTENNA ARRAY
TECHNICAL FIELD
[0001] The present invention relates to wireless
communications. More specifically, the present
invention relates to methods and systems for
determining and estimating various parameters of
incoming wireless signals using one or two antennas as
a synthetic antenna array.
BACKGROUND OF THE INVENTION
[0002] The communications revolution of the late 20th and
early 21st century has fuelled a need for better,
faster, and more useful communications devices.
Currently, there is a need for more efficient and more
effective methods for determining the parameters of
incoming wireless signals. The need is most acute in
the wireless communications industry but such
technology can also be applied to military uses.
[0003] Previously, to ensure proper determination or
estimation of the parameters of an incoming signal,
various antenna arrays have been used in conjunction
with many varied methods. Some of the previous work in
this field are as follows, all of the following being
hereby incorporated by reference:
H. L. Van Trees, Optimum Array Processing, Part IV of
Detection, Estimation, and Modulation Theory, 1st ed.,
John Wiley Inc, 2002.
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S. Charndran, Advances in Direction of Arrival
Estimation, Artech House, 2006.
R. 0. Schmidt, "Multiple emitter location and signal
parameter estimation," IEEE Transactions on Antennas and
Propagation, Vol. AP-34, NO.3. 1986, pp.276-280, March
1986.
R. Roy, T. Kailath, "ESPRIT- Estimation of Signal
Parameters Via Rotational Invariance Techniques" IEEE
Transaction on Acoustics, Speech and Signal Processing,
VOL.37, NO. 7, 1989.
B., Ottersten, M. Viberg, and T. Kailath, "Performance
analysis of the total least squares ESPRIT algorithm,"
IEEE Transactions on Signal Processing, vol. 39, no.
5,May 1991.
A. L. Swindlehurst, B. Ottersten, R. Roy, T. Kailath,
"Multiple invariance ESPRIT," IEEE Transactions on
Signal Processing, vol. 40, no. 4 April 1992.
Y. L. Jong and M. Herben, "High-resolution angle of
arrival Measurement of the mobile radio Channel," IEEE
Trans. Antennas Propagat., vol.47, no.11, pp.1677-1687,
November 1999.
Jong, Yvo L.C. de (2001) Measurement and Modeling of
Radio wave Propagation in Urban Microcells, PhD Thesis,
Department of Electrical Engineering, University of
Technology (EUT), Netherlands.
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A. Broumandan, T. Lin, A. Moghaddam, D. Lu, J. Nielsen,
G. Lachapelle, "Direction of Arrival Estimation of GNSS
Signals Based on Synthetic Antenna Array,"ION GNSS 2007,
Fort Worth, TX, 25-28 September 2007.
J. Pierre, M. Kaveh, "Experimental Performance of
Calibration and Direction-Finding Algorithms,"
Acoustics, Speech, and Signal Processing ICASSP, 1991.
C. M. S. See, "Sensor Array Calibration in the Presence
of Mutual Coupling and Unknown Sensor Gains and Phases,"
Electronics Letters, IEEE, Vol. 30, No. 5, March 1994.
F. Li, R. J. Vaccaro, "Sensitivity Analysis of DOA
Estimation Algorithms to Sensor Errors," IEEE
Transactions on Aerospace and Electonic Systems, Vol.
28, No.3 July 1992.
A. L. Swindlehurst, T. Kailath, "A Performance Analysis
of Subspace-Based Methods in the Presence of Model
Errors, Part I: The MUSIC Algorithm," IEEE Transactions
on Signal Processing, Vol. 40, No. 7, July 1992.
J. C. Liberti JR. and Theodore S. Rappaport, Smat
Antennas for Wireless Communication. Prentice Hall TPR,
1999.
S. M. Kay, Fundamentals of Statistical Processing,
Volume I: Estimation Theory, Prentice Hall, 1993.
M. Wax, T. Kailath, "Detection of Signals by Information
Theoretic Criteria," IEEE Transactions on Acoustics,
Speech, Signal Processing, Vol. ASSP-33, pp. 387-392,
1985.
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J.J. Caffery, G.L. Stuber, "Subscriber Location in CDMA
Cellular Network," IEEE Transactions on Vehicular
Technology, Vol. 47, No.2, 1998.
J.J. Caffery, Wireless Location in CDMA Cellular Radio
Systems. Kluwer Academic Publishers, Boston, 2000.
A. Moghaddam, Enhanced Cellular Network Positioning
Using Space-Time Diversity. MSc Thesis, Department of
Geomatics Engineering, The University of Calgary,
Calgary, Canada, 2007.
B. Allen, M. Ghavami, Adaptive Array Systems
Fundamentals and Applications, John Wily and Sons, Ltd,
2005.
A. Broumandan, T. Lin, A. Moghaddam, D. Lu, J. Nielsen,
G. Lachapelle, "Direction of Arrival Estimation of GNSS
Signals Based on Synthetic Antenna Array,"ION GNSS 2007,
Fort Worth, TX, 25-28 September 2007.
R. Roy, T. Kailath (1989) "ESPRIT- Estimation of Signal
Parameters Via Rotational Invariance Techniques" IEEE
Transaction on Acoustics, Speech and Signal Processing,
VOL.37, NO. 7
E. Gonen, M. Mendel, "Application of Cumulants to Array
Processing-Part III: Blind Beamforming for Coherent
Signals," IEEE Transactions on Signal Processing, Vol.
45, No. 9, September 1997.
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J. Jones, P. Fenton, B. Smith, "Theory and Performance
of the Pulse Aperture Correlator," Proceedings of ION
GPS, 2004.
Parsons, J. D.: `The Mobile Radio Propagation Channel',
(John Wiley & Sons LTD, 2nd ed. 2000)
Rensburg, C., and Friedlander, B.: `Transmit Diversity
for Arrays in Correlated Rayleigh Fading', IEEE Trans.
Vehicular Tech., Vol.53, No. 6, pp.1726-1734, Nov 2004
Kim, S.: `Acquisition Performance of CDMA Systems with
Multiple Antennas', IEEE Trans. Vehicular Tech., Vol.
53, No. 5, pp. 1341-1353, September 2004
Choi, S. and Shim D.: `A Novel Adaptive Beamforming
algorithm for a smart antenna system in a CDMA mobile
communication environment', IEEE Trans. Vehicular.
Tech., Vol. 49, No. 5, pp. 1793-1806, September 2000
Hyeon, S., Yun, Y., Kim, H. and Choi, S.: `Phase
Diversity for an Antenna-Array System with a Short
Interelement Separation', IEEE Trans. Vehicular Tech.,
Vol. 57, No. 1, pp. 206-214, Jan 2008
Kay, S. M.: `Fundamentals of Statistical Signal
Processing Detection Theory' (Prentice-Hall, Inc, 1998)
Fulghum, T. L., Molnar, K. J. and Duel-Hallen, A.: `The
Jakes Fading Model for Antenna Arrays Incorporating
Azimuth Spread', IEEE Trans. Vehicular Tech., Vol. 51,
No. 5, pp. 968-977, September 2002
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Liberti, J. and Rappaport, T. S.: `Smart Antennas for
Wireless Communications: IS-95 and Third Generation CDMA
Applications, Prentice Hall, 1999)
[0004] While antenna arrays have been found to be useful, the
size of multi-element antenna arrays preclude the use
of such devices in current devices. Smaller systems
would be useful and can be deployed in current handheld
devices.
[0005] In signal detection applications, an incoming signal
used in terrestrial or indoor wireless communication
links typically propagates from the transmitter to a
receiver over multiple reflective paths with a with a
consequence of a random variation in the complex
amplitude of the received signal. When the receiving
antenna is located in a diffuse multipath scattering
environment, fading appears to be a random function of
antenna location conforming approximately to Rayleigh
fading statistics with spatial decorrelation intervals
of less than the carrier wavelength of the signal. If
the receiver uses a single stationary antenna, then a
substantial fading margin is required to ensure
reliable signal detection. To reduce the fading margin
required, the receiver can use multiple spatially
separated antennas that exploit either the spatial
diversity or beamforming abilities that are inherent
properties of discrete antenna arrays. As noted above,
multiple element antenna arrays are incompatible with
current devices due to their physical size.
[0006] One parameter of incoming signals that can be critical
is time of arrival. Time Of Arrival (TOA) of a signal
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is a fundamental observable in most positioning
applications. The position of the mobile station (MS)
in 3-dimension space can be estimated by four or more
independent TOA measurements from base station
transmitters that are spatially separated with known
locations in the vicinity of the MS. However, the
coexistence of the multipath components along with the
desired line of sight (LOS) signal component typically
causes large errors in the estimation of the TOA
observables by the MS which maps into large positional
errors. CDMA signaling has a practical advantage of a
sizeable bandwidth which allows for partial resolution
of the LOS and corrupting multipath components.
However, TOA measurement errors on the order of 1 psec
are commonly encountered which typically result in
positional errors of several hundred meters. To meet
the requirements of applications that require accurate
position estimation on the part of the MS, lower
deviation and bias of the TOA observables is required.
To achieve this requires mitigation of the distortions
caused by the existence of the multipath components.
[0007] Significant research efforts have been expended on
using spatial information from multiple receiver
antennas. Classical beam forming and null steering
algorithms have been explored which are effective but
require an antenna array consisting of multiple
antennas which does not fit the form factor of the
handheld communications device. In addition, the
additional analog signal processing is a limiting
factor in this context. There is therefore a need for a
solution that has the advantages of antenna array
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processing but without the unwieldy hardware
implications of a multi-antenna array. One option would
be a synthetic array consisting of a single low gain
antenna conformal with the physical constraints of the
handheld MS device. Spatial array processing techniques
for single antenna synthetic arrays have been deployed
for several decades however, these methods require that
the antenna be translated through a trajectory known to
the receiver with very good precision. Incorporation of
such solutions into a communications handset would
require a precision measurement capability in the form
of an inertial device. Such an inertial device would
be difficult to implement into a handheld device.
[0008] Another problem of interest in many signal-processing
applications is the estimation of signal parameters
from a set of data measurements. High-resolution Angle
Of Arrival (AOA) estimation is an important issue in
many applications such as radar, sonar, spatial
filtering and location estimation specifically enhances
the 911 requirement (E-911) in wireless emergency
services. There have been several high-resolution AOA
estimation methods including the multiple signal
classification (MUSIC) and the Estimation of Signal
Parameters via Rotational Invariance Techniques
(ESPRIT) algorithms. Although the MUSIC algorithm is
widely used, it has certain practical implementation
issues when compared with ESPRIT. The MUSIC algorithm
requires prior calibration of the antenna elements such
as the phase, gain as well as the positions of the
elements. In addition, a computationally expensive
search is required over the processed parameter space.
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AOA estimation with the MUSIC algorithm requires the
exact knowledge of position of the elements. However,
the specific array geometry of standard ESPRIT
algorithm requires twice the number of sensors in
comparison with the MUSIC algorithm.
[0009] In particular, applications such as handset-based
geolocation estimation and determining direction of
interfering signals, portability of receiver is a
primary issue generally precluding the use of several
antenna elements as required for AOA estimation. To
overcome this restriction, antenna array can be
synthesized by moving antennas in an arbitrary
trajectory. Some researchers have shown an application
of using synthetic array with uniform circular array
(UCA). They have used UCA-MUSIC based on phase-mode
excitation with beam-space processing to determine
multipath contributions in wireless mobile propagation
environments. In one research implementation, a
mechanical lever arm was used to synthesize a circular
array by using a single rotating antenna with the
constant speed. AOA estimation using a synthetic array
has significant advantages because inter channel
phases, and gains and mutual coupling between antenna
elements do not affect the AOA estimation. However,
the basic assumption of synthetic arrays with the MUSIC
algorithm, the stationarity of the radio channel, is
not always possible in real mobile communication
systems.
[0010] Several methods have been developed to implement a
synthetic array for use in AOA estimation. However,
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these methods have drawbacks that limit their
applicability. As an example, in one implementation,
users cannot carry the precise moving motor that one of
the methods requires to synthesize the antenna array.
It should, however, be noted that using a known
constant speed rotating motor comes from the inherent
restriction of the MUSIC algorithm (the requirement
that the sensor position has to be precisely
calibrated). When implementing the MUSIC AOA estimation
algorithm, the entire array manifold (phase, gain, and
sensor positions) has to be perfectly known. Instead of
using a precisely moving motor, other researchers have
extended the synthetic array idea by using external
sensors, namely Inertial Measurements Units (IMU) which
consists of accelerometers and gyroscopes, as a
potential solution. Instead of using a predefined
array shape, The external sensors are used to estimate
the trajectory of the antenna in the synthetic array.
Unfortunately, this solution still has issues and
shortcomings. Trajectory estimation by the IMU is
restricted to the level of accuracy that is dictated by
the class of IMU and type of motion of the trajectory.
On the other hand, the element position perturbation
that the MUSIC algorithm can tolerate depends on the
wavelength of the frontwaves. Experimental results
obtained by using signals in 1.5 GHz band (20 cm
wavelength) revealed acceptable results of trajectory
estimation when using medium-cost IMUs. However, such
results were only for tightly controlled trajectories
which had predefined and gentle motions at a constant
speed. Truly arbitrary trajectories were not tested
and were noted as being quite difficult to estimate.
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[0011] Based on the above, there is therefore a need for
systems and methods that mitigate if not overcome the
shortcomings of the prior art.
SUMMARY OF INVENTION
[0012] The present invention provides systems and methods
related to wireless communications. A system for
estimating parameters of an incoming signal is
provided. At least one antenna is coupled to at least
one suitable receiver. The antenna(s) are spatially
translated in an arbitrary trajectory. As the
antenna(s) is being spatially translated, a data
processing means samples the incoming signal at set
intervals based on a clock signal provided by a system
clock. By sampling the incoming signal at different
times at different spatial locations on the arbitrary
trajectory, the system acts as a synthetic antenna
array. The different samplings of the incoming signal
at different times and positions provide signal
diversity gain as well as different readings which can
be used to estimate and/or calculate various parameters
of the incoming signal. The different samplings can be
used to detect the incoming signal, estimate its angle
of arrival, estimate its time of arrival, as well as
other parameters.
[0013] In a first aspect, the present invention provides a
system for determining at least one parameter of an
incoming wireless signal, the system comprising:
- at least one antenna
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- at least one sensor
- a clock for providing a clock signal
- data processing means
wherein
- said at least one antenna is spatially translated in
an arbitrary trajectory
- said data processing means samples data from said
incoming signal at intervals based on said clock signal
as said at least one antenna is spatially translated
through said arbitrary trajectory
- said data processing means determines said at least
one parameter of said incoming signal based on said
samples and input from said at least one sensor.
[0014] In a second aspect, the present invention provides a
synthetic antenna array system for estimating at least
one parameter of an incoming signal, the system
comprising:
- two antennas for receiving said incoming signal
- a clock for providing a clock signal
- data processing means for processing data from said
incoming signal to estimate said at least one parameter
wherein
- said incoming signal is sampled by said data
processing means in specific predefined intervals, said
intervals being based on said clock signal
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- said two antennas are spatially translated in any
arbitrary trajectory while said incoming signal is being
sampled.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The invention will be described with reference to the
accompanying drawings, wherein
FIGURE 1 is a block diagram of a system according to
one aspect of the invention;
FIGURE 2 is a diagram illustrating the use of early and
late correlators for estimating time of arrival;
FIGURE 3 is a diagram describing the multipath error
envelope for narrow and double delta correlators;
FIGURE 4 is a diagram illustrating the situation where
there is one multipath component in a TOA estimation;
FIGURE 5 shows the multipath error envelope of the
narrow and double delta correlators shown in Fig. 4;
FIGURE 6 is a diagram showing the radiation pattern of
a 4-element Uniform Linear Array;
FIGURE 7 is a diagram illustrating the correlation
function before and after applying beamforming and
null-steering when performing a TOA estimation;
FIGURE 8 illustrates the average SNR required for
stationary and moving antennas for signal detection;
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FIGURE 9A shows average SNR as a function of M when
detecting signals using one aspect of the invention;
FIGURE 9B illustrates G as a function of M when
detecting signals using one aspect of the invention;
FIGURE 10A illustrates the optimal value for G as a
function of the probability of a successful detection
for a fixed probability of a false alarm;
FIGURE 10B illustrates the corresponding value for an
optimum M; and
FIGURE 11 shows the normalized correlation function
after calibration when the antenna array was
perpendicular to the calibration source.
DETAILED DESCRIPTION OF THE INVENTION
[0016] Referring to Figure 1, a block diagram of the system 10
is illustrated. As can be seen, the antenna 20 is
coupled to a receiver 30. The receiver 30 is coupled
to a sensor block 40 as well as to a data processing
means 50. A clock 60 provides an internal clock signal
to the receiver 30, the sensor block 40, and the data
processing means 50. The antenna 20 is spatially
translated in an arbitrary (and possibly random)
trajectory. As the antenna traverses the trajectory,
the data processing means, through the receiver,
samples an incoming signal at specific intervals.
Whether an interval has passed or not is determined by
the clock signal from the clock. The sensor block 40
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contains sensors that can provide data regarding the
trajectory to the data processing means if the
calculations performed by the data processing means
requires it. It should be noted that while Fig 1 shows
a single antenna/receiver pair, a doublet or two
antennas/receiver pair may also be used as well as
other configurations.
[0017] In one variant of the system, the incoming signal is
continuously sampled in short bursts. In another
variant, the signal is despread or processed to obtain
samples of the channel gain for the specific signal.
In yet another variant, the signal is sampled
continuously as the antenna is moved without
interruption.
[0018] Regarding the system, it is essentially a synthetic
antenna array. A synthetic array generally implies a
single antenna that is physically translated in space
over an aperture interval. The signal collected at the
output of this antenna during the interval that it is
translated is used in the subsequent signal processor.
By weighting the response appropriately an equivalent
scanning beam can be created such that the antenna can
achieve high directivity commensurate with the physical
size of the aperture that the antenna was swept through
during data collection.
[0019] To achieve this high directivity it is convenient for
the antenna to be associated with sensors from which
the precise spatial trajectory can be estimated. It is
preferred that the estimate of the trajectory to be
very accurate to avoid the beam from defocusing. Such
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sensors are usually based on a combination of GPS
(GNSS) and inertial devices such as rate gyros and
accelerometers. It should, however, be noted that in
other applications, sharp focus is less of an issue.
For these applications, diversity gain is sought
instead of gain through beam focussing or beam forming.
[0020] The synthetic array concept being used in the invention
is where a single antenna is swept through a spatial
trajectory that is arbitrary and random. Usually the
trajectory would be generated by a user sweeping his
hand through a smooth arc. However, the trajectory
could also be less deliberate motion with the antenna
attached to the user's helmet or clothing in some way.
Alternatively, the system could be deployed in a
handheld form factor with the handset being swept
through some smooth trajectory. In another
alternative, the system could be co-located with a user
inside a moving vehicle or platform.
[0021] The system could use sensors or a set of devices used
for estimating the trajectory. These would be
inexpensive MEMS type accelerometers and rate gyros.
Optionally a small CCD camera could be used to augment
the sensor array output. Also possible is a GNSS
receiver that processes the GNSS signals captured by
the antenna.
[0022] Also associated with the system is a free running clock
typically based on an ovenized crystal oscillator. The
oscillator cannot be locked to any reference as the
carrier phase of the moving antenna is measured with
respect to the phase of this oscillator. In one
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alternative, there may be a known signal of known
direction relative to the antenna trajectory that is a
line of sight plane wave signal at the location of the
system. In principle the system can jointly determine
the trajectory and the clock phase.
[0023] The output of the system processing can have a variety
of possibilities. However, the general objective is to
use the snapshot signal collected to generate at least
one parameter or perhaps a set of sufficient statistics
such that the desired output parameter can be detected
or estimated. For example it may be desired to detect
if the signal is present or not as part of a generic
signal acquisition scheme. The system may be used to
determine the delay of the signal relative to a locally
generated reference. It can also be to used estimate
the angle of arrival of the signal relative to the
estimated trajectory of the antenna.
[0024] For time of arrival estimations, the antenna's
trajectory need not be estimated as the use of fourth
order cumulants simplifies the calculations.
[0025] Spread spectrum signals, due to their wide bandwidth,
are well suited for TOA estimation. One property of
Pseudo Random Noise (PRN) sequences used to modulate
CDMA signals is that their cross-correlation is almost
zero except when the lag is 0. Based on this property,
one can estimate the time delay between transmitter and
receiver. The correlation function using PRN sequences
with a rectangular pulse shape is a triangular shape
function with the spread of Tc where Tc is the
chipping time of the spreading code. The resolution of
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the conventional correlation method is limited to the
sampling rate of the receiver. One possible approach to
increase the resolution of the TOA estimation is by
using two equally spaced correlators called early and
late correlators which is shown in Fig. 2 by E1 and L1
respectively. By comparing the early and late
correlator power of the correlation function, one can
estimate the exact TOA in the absence of multipath and
noise with an ideal receiver response. A double delta
correlator utilizes two pairs of correlators rather
than one to estimate the LOS (line of sight) signal
delay by compensating the multipath influence
significantly. If multipath signal exists with the LOS
signal, the TOA estimation from standard correlation
methods will be influenced by multipath, since the
magnitude of the rising edge slope is different to the
one of the falling edge in the autocorrelation peak.
The basic idea of double delta correlator is to
introduce a correction term, which can compensate for
the slope difference between the rising and the falling
edge due to multipath. Fig. 2 shows the correlation
function, and the early and late points. The following
derivation represents the correction term to the
conventional correlation function based on the double
delta correlator concept.
2 (E1 - L,) -- ( ', -- L, ] d
(El - E, + L, -.L2) 2 (1)
[0026] where d is early and late correlator spacing and t
gives the correction term. To evaluate the performance
of early-late TOA estimators, multipath error envelop
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is a common curve to demonstrate the performance of
different approaches in presence of multipath
propagation. Fig. 3 describes the multipath error
envelop for narrow and double delta correlator.
[0027] There is just one multipath component with 3 dB
attenuation and correlator spacing, d, is 0.1 chip.
Based on the results of Fig. 3, the multipath
propagation can cause up to 0.025 chip error in TOA
estimation. All early-late TOA estimators, including
narrow correlators and double delta correlators, try to
estimate the correct position of the correlation peak
based on the triangular shape of the auto correlation
function. In the absence of multipath propagation,
these methods are the best options in terms of
complexity and accuracy.
[0028] The performances of the early-late correlators are
usually evaluated in the presence of one multipath
component with weaker signal power with respect to the
LOS signal. The limitation of the early-late
correlators comes into view when there are several
multipath components. Consider the situation where
individual multipath components has stronger energy
than the LOS signal (e.g. indoor environments) or the
combination of sub-chip multipath signals has stronger
energy than the LOS signals (e.g. in indoor, urban, and
under foliage). In these cases, the estimated TOA by
all peak estimator techniques (i.e. conventional
correlator, early-late correlator) becomes wrong. The
TOA estimation error depends on the delays of multipath
components with respect to the LOS signal. The errors
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can be in the order of one chip. Fig. 4 shows the
situation where there is one multipath component. The
multipath signal is stronger than the LOS component and
has one chip delay from the direct signal. The peak of
the correlation function shown in Fig. 4, belong to the
multipath component. All peak-based TOA estimation
techniques consider the highest point of the
correlation function as a rough estimate of the LOS
signal which is off by one chip from LOS component.
Fig. 5 shows the multipath error envelop of the narrow
and double delta correlators for the situation
described in Fig. 4. Fig. 5 shows that the strong
multipath component destroys the performance of
correlation based TOA estimators.
[0029] To formulate the signal model, consider a multipath
wireless channel scenario, in which the desired signals
form different paths to a receiver. These signals are
received by an M-element synthetic array with an
unknown array geometry. One can assume N narrowband
signals which includes the direct signal and several
delayed and attenuated replicas of line of sight (LOS)
signals. By narrowband, it should be noted that it is
meant that the reciprocal of the baseband bandwidth is
much longer (in time) than the time delay across any
two points of the antenna's trajectory. The
measurement system is corrupted by additive noise with
an unknown spatial correlation matrix. The M array
output vector can be shown by
x(t) = As(t)+n(t) (2)
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where
A = ta(O ), (O7 , ... , a(0 )
S(t) = y3(t).s' (0I
(3)
[0030] A is MxN unknown steering matrix. s is the Nxl
signal vector where sl(t) corresponds to the desired
signal. n is an additive noise vector at the antenna
elements and could be Gaussian, non-Gaussian, or a
combination of Gaussian or non-Gaussian vectors.
[0031] Spacing between each elements of the synthetic antenna
array is preferably less than half a wavelength of the
impinging wave-fronts to mitigate aliasing. However,
any spacing can be considered. With spacing less than
half a wavelength of the impinging wavefronts, the
samples are spatially correlated. If the spacing is
greater than the half wavelength, the samples tend to
become uncorrelated. If, on the other hand, the system
is used mainly for estimating the angle of arrival,
then the half a wavelength limitation is preferred.
[0032] In beamforming techniques, the sensor outputs are
weighted with specific coefficients to pass desired
signals without distortion, while mitigating the
effects of the interference and undesired signals. In
delay estimation applications, it is critical to
mitigate the effects of multipath and jammer signals as
shown in previous section. Consider a situation where
field signals are measured by a 4-element Uniform
Linear Array (ULA). In this case, based on the
beamforming techniques by coherently combining the
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spatial samples of desired signal and uncorrelated
multipath component assumption in different antennas, a
6 dB gain is achievable in the direction of the desired
signal. However, unwanted signals are uncontrolled in
this scheme. As it was shown in the previous section,
multipath mitigation is as important as enhancing the
desire signal. Fig. 6 represents the radiation pattern
of a 4-element Uniform Linear Array (ULA) when the main
beam steers at 80 degree, and one null steers to 140
degree. This figure shows that more than 20 dB
rejections are achievable for signals arriving from
unwanted direction. Therefore, by utilizing null-
steering technique a deep null can be placed in
direction of multipath and interference signal.
[0033] Consider A as the steering matrix of the array. Assume
sl(t) is the desired signal which should be recovered
without distortion and {a(82), ...,a(OK) } represents the
unwanted signals. The beamforming and null-steering
weights, which pass desired signal without distortion
and attenuate unwanted signals can be defined as
firth = cA'4 (AA11)'
(4)
where c is the 1 x N vector
C [L...]
(5)
[0034] The following describes the modified version of
cumulant based blind steering estimation adopted for
TOA estimation algorithm with CDMA signals. Consider a
CDMA signal structure, which utilizes an orthogonal
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Pseudo Random Noise(PRN) code for modulating the
transmitted signals.
[0035] Due to using orthogonal codes, the only remaining
signals after the de-spreading process is the desired
signal and several of its replicas. We are assuming
here that the desired signal and multipaths components
are correlated but not coherent (i.e. they are
partially correlated) and for the number of impinging
signal including LOS and multipaths, N is less than the
number of sensor elements M (N<M). The main goal herein
is that of extracting the desired signal embedded in
interference signals and multipath propagation. To use
beamforming and null-steering noted above, one has to
estimate the signal steering matrix, A. Due to the
correlated multipath propagation without any knowledge
of the array geometry or of the calibration
coefficients, it is difficult to estimate the desired
signal steering vector using second order statistics.
Fourth order cumulants are used for estimating the
steering vector. The fourth-order cumulant matrix C1 can
be defined as:
cuin x,(t), x1 (t )' x(t), x(t )H
y Ep4jA(1,1)aaH AAA"
(6)
where xi (t) are the outputs of the ith sensor and x(t) is
the array output. A is the array steering matrix, P4,,,i are
the signal fourth order cumulants and
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Where cum is the fourth order cumulants which can be
defined as
CLIIII((1),X;(),k (t )x (t)
E { t (t) X {.t) k t )xN (t)
-E {.t: (I)--, (i)}E{-''(I)t"(I) - 11
{
E (t )- (I)}E (I) .
_E{x,(t)x (t) E x6(t) . (t)}
(7)
[0036] Similarly, C2 can be represented by
-- 4 , (2 i.)A*(1J)ata A AA"
t=x (8)
where
D=ctia A(2, 1) A(21 )
g 1.
(?, 1) .., (1, N )
[0037] It should be noted that it is possible to estimate A
and D matrices within a complex constant by the
rotational invariance property (originally introduced
for the ESPRIT algorithm as will be explained below).
In fourth order cumulants based blind steering
estimation, the rotational invariance property is
achieved without any need of the identical copy of the
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array. Steps toward blind steering matrix estimation
can be shown as
1) Estimate M x M cumulant matrix
C1 =C:LiOn (x, (t), Xi (t), X, 01 X 0,
C =cLIIm1 (x(t),x(t)x(t),x(t)")
(9)
2) Put the Cl and C2 matrix into a 2MxM matrix C as C=[ Cl
C2 ]T
3) Perform SVD (singular value decomposition) of C.
Replace the first 2MxN submatrix of the left singular
vector of C into matrix U1.
4) Partition Ul into two MxN matrices Ulf and U12
5) Perform SVD of [U11rU12] and keep the last N right
singular vectors and put it into 2NxN matrix defined by F
6) Partition F into two N x N matrices F= [Fx,Fy]T
7) keep the eigenvector, E and eigenvalues, A of the -FXFy-1
matrix
8) the following relation gives the steering matrix
within a diagonal matrix.
A=IT( ,,E+UUEAt)
2 (10)
where 'P is an arbitrary diagonal matrix.
[0038] This TOA estimation method can be summarized as
follows:
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1) Estimate the signal space dimension (sophisticated
approach such as AIC and MDL can also be used)
2) Estimate the steering matrix based on the blind
fourth order cumulants
3) Steer the main beam of the beamformer to the first
estimated steering vector and place nulls in direction
of other estimated steering vectors
4) Repeat step 3 for all estimated steering vectors by
changing c in equation (5) above.
5) Compare all possible correlation function described
in step 3 and 4 and choose the one with minimum
propagation delay as the desired signal.
[0039] Steps 4 and 5 are performed to detect the LOS component
by assumption that the LOS signal arrives before
multipath components and has minimum propagation delay
(presence of the LOS signal is assumed). The
correlation function before and after applying
beamforming and null-steering technique is shown in
Fig. 7. The solid curve represents the correlation
function of the received signal which composed of two
components: LOS and multipath. The multipath component
has 3 dB more power than the LOS signal and arrives
with 1.2 chip delay respect to the LOS signal. The
estimation TOA based on the correlation peak has
approximately 1.2 chip error. The dashed curve in Fig.
7 represents the normalized correlation function after
beamforming and null-steering process. The null-
steering process completely removed the multipath
component so the remaining signal is LOS.
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[0040] For signal detection applications, experiments have
shown that spatially translating a single antenna in an
arbitrary trajectory while sampling the incoming signal
(to provide a synthetic antenna array) provides
calculation gains versus a single stationary antenna.
The calculations for the probability for false alarm
detection and for a positive detection are provided
below.
[0041] Assume that the phase center of the single antenna is
coincident with a point in a three-dimensional spatial
coordinate system identified by the position vector p.
The complex baseband signal representation of the
antenna output received signal is denoted as r(t). This
signal snapshot is processed to decode between the Ho
and H1 states. Under the Ho state, the signal is not
available and under the H1 state the signal and noise
are available. Under Hl the signal component of r(t)
is denoted as s(t,p) which is a function of time t and
the antenna position p. This signal is expressed as
s(t,p) = A(p)s0(t) where so (t) is the deterministic
(pilot) complex baseband component of the signal that
is known to the receiver and A(p) is the channel gain.
The received signal is corrupted with additive noise
which has an equivalent complex baseband representation
denoted by w(t). It is assumed that w(t) is a
circularly normal random process, independent of the
signal and has a power spectral density (PSD) that is
constant within the bandwidth of so(t). The conditional
representations of r(t) for a stationary antenna
located at p is expressed as
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r(t)?, A(p)sir(t)+iv(t
r(t) ,f,=lv(t) (11)
[0042] The receiver accumulates a temporal snapshot of r(t)
over the snapshot interval of tE[O,T] as introduced
beforehand. Based on the Rayleigh fading assumption,
A(p) is a zero mean circularly normal random variable
such that A (p) -CN(O, 62A) where - denotes the PDF of the
left hand side variable and CN(O,(y 2) signifies a
circularly normal PDF of mean p and variance a2 . As
sa(t) is known to the receiver, A(p)is circularly normal
and w(t) is spectrally white within the bandwidth of
so (t) , the optimal Neyman Pearson (NP) detection
processing is a matched filter based on correlation
with so(t)* followed by a magnitude squared operation
where 11* represents the complex conjugate operator.
This processing results in the decision variable
denoted as zo which is expressed as
T
Z=IxTI_ f r( )s" (tM
, Cll
3:1
(12)
where the intermediate variable XT is defined for
convenience.
[0043] The signal energy of so(t) is normalized such that
' T
s: (t)' tit 1
T
[0044] Since
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T tt
It .t)S.(t)Mtit - CAT (0,T.~V, f
0
the average SNR p can be represented as
T(74
(13)
[0045] This definition will be used in subsequent discussions
Without loss of generality, the normalization of TN, =l
can be imposed such that p=T2o'A which simplifies the
expressions to follow.
[0046] To determine the probabilities of a false alarm (Pfa)
and of a positive detection (Poet) for a stationary
antenna, (where y is a threshold)
P det O+P' 1
(14)
[0047] If one uses a synthetic array where the antenna is
moved along an arbitrary trajectory while the snapshot
data is being collected, the position vector to the
antenna location at time t from the origin is now
denoted as p(t). The signal component of the complex
baseband signal r (t) is written as s (t,p (t)) which is
a function of time, t and the antenna position, p(t)
which in turn is a function of t.
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[0048] If one were to compare the synthetic array case with
the case for a stationary antenna, the constraint T=
MAT will be imposed. Define tn, as the starting instance
of the mth subinterval that extends over the interval of
[ tm, tm+AT] for m e [ 1, 2,..., M] . AT is considered to be
sufficiently small such that A(p(t)) can be
approximated as constant over the interval of AT. The
signal captured in each subinterval is correlated with
so(t) resulting in a set of M spatial array samples
denoted by xm and given as and given as
bra. 1 Y p { t )) .S'rr + i `rrs
(15)
where
St)t
S" =
f I i'
t'( T)Sõ (t) fit
r
r (16)
it follows that xm forms a set of sufficient statistics
of the accumulated snapshot signal in terms of optimal
decoding between Ho and H1. After some manipulation, and
removing deterministic scaling and additive constants in
the uncorrelated signal environment, the Likelihood
Ratio Test (LRT) reduces to
X"" 12
?~a~t (17)
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which is normally referred to as the equal gain
combiner.
[0049] The Pfa and Pdet can be determined for a threshold y as
0 x- (18)
and
r
Price -X2 ~f
(19)
where Pfa is the probability of a false alarm and Pdet is
the probability of a true detection of the incoming
signal.
[0050] The target detection parameter Pfa is used in equation
(18) to determine the threshold y. This is used in
equation (19) with the target parameter Pdetto determine
the average SNR, p, required.
[0051] Given target detection parameters Pfa and Pdet, the
average SNR required for the stationary and moving
antenna, denoted as ps and pm, respectively, can be
evaluated.
[0052] Fig. 8 shows ps and p,, as a function of the target
parameters {Pfa, Pdet} with Pfa =0.01 for M=4. Consistently
for larger values of Pdet, pm is less than ps,
demonstrating the advantage of the synthetic array
compared to the stationary antenna. Also evident in
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Fig. 8 is that this advantage decreases as Pdet is
reduced. When Pdet is sufficiently low, ps is less than
pm, indicating that moving the antenna may be detrimental
to performance. However, the range where ps < pn, is of
negligible practical significance as Pdet is so low.
Fig. 9A shows the average SNR as a function of M with
the interesting observation that there is a global
minimum. There is therefore an optimum value of M for
which pn, is minimum and this is denoted as Mpt.
[0053] It is convenient to define G= p,s/pn, as the processing
gain of the synthetic array antenna processing relative
to the stationary antenna processing. In Fig. 9B, G is
plotted as a function of M for the corresponding case
represented in Fig. 9A. As expected G has a global
maximum at Mol,temphasizing the optimum choice of M. For
this analysis, uncorrelated samples of the channel gain
were assumed. This implies that the trajectory is
large enough that it can accommodate Mopt uncorrelated
channel samples.
[0054] As M is increased for the moving antenna, the diversity
gain increases. However, the incremental diversity gain
also decreases to small values as M becomes large. As M
is increased, the coherency of the snapshot signal is
reduced as each of the M subinterval components
constituting the overall snapshots are essentially
noncoherently combined. This eventually becomes the
dominant loss factor as M becomes larger. The
consequence of these factors is the existence of an
optimum value for M. Fig. 10A shows the optimal value
of G as a function of Pdet for a constant Pf,. As
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indicated earlier, G increases as Pdetis increased which
is due to the increased significance of diversity gain
as the target Pdet is increased. This is equivalent to
the increased significance of the diversity gain for
lower BER in communication systems. Fig. 10B shows the
corresponding value of MQPt with the interesting
observation that M,,t increases significantly for larger
Pdet= This implies that the trajectory of the antenna
has to be larger to achieve more uncorrelated spatial
samples of the channel gain. Note from Fig. 1OA that an
optimal G of 11 dB gain is attainable when Pdet=0.99
which is a practical target specification. Mptfor this
case is eight, which maps into a reasonable trajectory
size for carrier frequencies in the 1 to 2 GHz range.
[0055] For angle of arrival (AOA) estimations, an ESPRIT based
method, using a two-antenna (doublet) receiver is
disclosed below. While this scheme removes the
necessity for any mechanical moving motors or external
aiding sensors, experiments have shown that a sensor
that tracks the vector between the two antennas
improves the estimated AOA.
[0056] For this scheme, the doublet (two-antenna system with
separate receivers for each antenna) is spatially
translated in an arbitrary trajectory.
[0057] For this AOA estimation scheme, we can assume K
impinging signals from K different locations being
received by an arbitrary geometry array with M sensors.
Signals could be samples of a stationary random
stochastic process or be a deterministic function of
time. Signals are narrow-band processes whose source
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bandwidth is smaller than the reciprocal of the time
delay along the array. Far-field sources are assumed,
which there are not coherent (perfectly correlated).
Consequently, the received signal is a combination of
the LOS and reflected plane wave signals. The number of
incoming signals are less than the number of the
sensors (K<M). The received vector signals can be
represented by:
x(t) =A(O)s(t)+w(t) (20)
[0058] where x(t) is an Mxl observation vector, s(t) denotes
the vector of complex signal envelop at time t. A is
an MxK steering matrix for signals coming from
direction 9={ Al , 02 0K } .
rk`
.A (O) =[a 0,),a ) ...,a(OK)] (21)
[0059] w(t) is spatially and temporally white Gaussian noise
with variance of 02.
[0060] The correlation matrix of the observation vector is
R X = ' ;:Ps " + 1
(22)
where P. is the signal correlation matrix and I is an M x
M identity matrix.
[0061] For this discussion, superscript H indicates complex
conjugate transpose, T denotes the transpose operator,
and * indicates complex conjugate.
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[0062] While there are several versions of the ESPRIT
algorithm, in this discussion the Total Least Square
(TLS) version of the ESPRIT algorithm will be used. The
ESPRIT algorithm exploits a specific array geometry.
For simplicity consider a planar array consist of M
doublets (pairs of antennas) with arbitrary geometry
and phase and gain response. The sensors in each
doublet have the same pattern characteristics. The
intra doublet element spacing is identical for all
doublets. In addition, the connection axes of intra-
doublet sensors are parallel for all doublets
(translation invariance property). The array
configuration of the ESPRIT algorithm can be
represented by two sub-arrays which each doublet has a
member in each sub-array. Each sub-array is the replica
of the other one by the known physical displacement d.
In this scenario, each sub-array consists of sensors
with arbitrary phase and gain characteristics.
[0063] The output of each doublet can be represented by the
equation below:
x,(t) = As(t)+n,(t)
x2(t)=Ass(t)+n2(t)
(23)
where s(t) is the signal vector and nl(t) and n2(t) are
noise samples in each sub-array. ' is defined as a KxK
matrix that relates the measurement from the first sub-
array to the other one and contains the AOA information
1 r:i t2rd t,
~f ~ 51[14K
yy y ti311
i =' do agle f = = I.
(24)
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A is the wavelength of the impinging signals and 6 is
the AOA of arrival signals relative to the doublets
connection vector. Therefore, the AOA can be extracted
by estimating the W matrix. Combining the outputs of the
sub-arrays to a single array yields
x (1) AT 2(
A t + nI t) (25)
[0064] The ESPRIT algorithm relies on the estimation of the
signal sub-space. The signal sub-space can be estimated
through eigen analysis of the correlation matrix
defined by
.A'RSA '11 4-(7-1 (26)
[0065] The K eigenvector corresponding to the K largest eigen-
values span the signal subspace Es={ej,e2, ..., eK}. Eg
can be represented by
Es
X2
L E (27)
[0066] The range of Es is equal to the range of A' so that
there exists a nonsingular matrix T to satisfy ES=A'T.
F=E# E E#
By defining .1 ', where " represents the pseudo
inverse of EX' it can be shown
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TFT-1
(28)
[0067] Therefore, the AOA can be estimated from the
eigenvalues of F. The proof of TLS ESPRIT algorithm is
extensively described in the literature. The steps
toward AOA estimation by the ESPRIT algorithm can be
summarized as follows:
1) Estimate the correlation matrix from N independent
measurement
2) Calculate the signals subspace dimension
3) Estimate the signal subspace E. from partitioning the
eigenvectors
4) Compute the eigenvectors of
E
[E, I E EA E*
E 2 (29)
and partition E.
Ell E12
E 21 E22- (30)
t
5) Estimate the eigenvalues T" of 22
6) The AOA can be estimated by
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11-1 {ar@:
Ok = 12-gdl
(31)
[0068] Equations (14) and (18) reveal that for AOA estimation
with the ESPRIT algorithm, array manifold information
is not required. This property has several benefits,
which are listed below.
[0069] Pl) array calibration is a critical procedure in high-
resolution AOA estimation. Several articles in the
literature have discussed methods to mitigate phase and
gain differences among different channel of the sensor
arrays. In the ESPRIT algorithm context, the
calibration process is just limited to the doublet
phase, gain adjustment. Experimental results showed
that AOA estimation by the ESPRIT algorithm is not
sensitive to the gain differences. This decreases the
number of parameters to be estimated.
[0070] P2) Equation (18) is a key relationship in developing
of the ESPRIT algorithm which suggests that eigenvalues
of F is equal to the diagonal elements of T. The
estimated AOA only depends on intra-doublet element
spacing d and not on inter-doublet spacing. This
property increases the flexibility of the array
geometry and array aperture extension with the fixed
number of sensors.
[0071] The specific properties of the ESPRT algorithm make it
a proper candidate for AOA estimation with the
synthetic antenna array. In this case, just one doublet
(two sensors with constant spacing during data
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collection) is required for synthesizing the whole
array. During the data collection, the receiver
collects spatial and temporal samples. The only
constraint in synthetic ESPRIT algorithm is the
translational invariance moving of a doublet. Neither
rotating with mechanical motor nor external sensors is
required for a synthetic ESPRIT array. The important
assumption in AOA estimation with the synthetic MUSIC
algorithm is stationarity of the communication channel.
In actual cases, the stationarity of communication
channels due to passing vehicles and movements of trees
is impossible. One more advantage of ESPRIT algorithm
over MUSIC algorithm in implementations using a
synthetic array is that the ESPRIT algorithm assumes
sensors in different doublets have various patterns.
Time varying multipath channel in the presence of
direct signals can be modeled by phase and gain
differences in different doublets. Therefore, a
synthetic array implementation of the ESPRIT algorithm
is more suitable than a synthetic array MUSIC algorithm
implementation when in the presence of time varying
channels.
[0072] Experimental setups to test the AOA estimation method
used specific configurations of equipment. Pilot
signals of downlink channel of CDMA IS-95 standard
continuously broadcasts a known signal to provide
Mobile Stations (MSs) a robust time, frequency, and
phase reference for demodulation in other channels. The
pilot channel has no data modulation and consists of
only in-phase and quadrature phase pseudorandom noise
(PRN) codes. Due to the higher power of the pilot
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channel with respect to other channels, signals of
pilot channels are preferred for AOA estimation
purposes. All BSs use the same PRN code, distinguished
by the different code offsets. The receiver is tuned to
capture CDMA signals with the bandwidth of 1.25 MHz
modulated by 1.2288 Mchip/s PRN sequence with the
period of 215 chips at 1947.5 MHz.
[0073] Signals received at antennas are amplified, filtered,
down-converted and sampled. In order for BS detection
for comparing AOA estimation results with the real
angle between antenna array and BSs, GPS time
synchronization is necessary. In CDMA IS-95 cellular
networks, all BSs are synchronized with the GPS 1 Pulse
Per Second (1PPS) signal. A 1PPS signal is used to
control the starting edge of the pilot code at each BS.
Because of oscillator offset and Doppler shift, there
is some residual frequency between the received signal
frequency and the frequency of the local oscillator
used to down-convert the received CDMA signal. This
residual frequency is not completely compensated for in
the CDMA receiver and requires a signal acquisition
process. The acquisition process is a 2-dimensional
search in both frequency and time in order to
compensate for a residual frequency offset and to
determine time delay between transmitter and receiver.
[0074] As noted above, calibration is a useful stage for
utilizing high-resolution AOA estimation algorithms.
The synthetic array implementation of the ESPRIT
algorithm consists of a two-channel receiver. Due to
utilizing different components in different channels,
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each channel has distinctive phase and gain response.
Experimental results have shown that the ESPRIT
algorithm, in contrast to the MUSIC angle estimator, is
not sensitive to gain errors. Therefore, only phase
calibration or equalization will suffice. A simple and
practical phase estimator to equalize the phase
imbalance between channels can be used. In the AOA
estimation concept, directions of incident signals are
estimated based on the phase difference among different
antennas. Therefore, the relative phase of sensors with
respect to the calibration point is important while the
absolute phase of each channel is not. Based on this
concept, in one experimental setup, a two-channel
receiver was placed on the roof of a building that has
access to the line of sight signals from a CDMA BS. We
have assumed that the phase differences among channels
are angle independent. The maximum likelihood phase
estimator is used to calculate the phase of each
channel at the output of the correlation function. The
resulting calculation is as follows:
t a n - j 1,2
(32)
where (pi is the estimated phase at the output of each
channel and xi is the received signal from ith sensor
after a de-spreading process. The calibration process
equalizes the phase differences respect to the
calibration source. For example, if the array is
perpendicular to the calibration source, in the ideal
case, different channels have to receive impinging
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signals with the same phase. The phase calibration
process compensates for this phase difference with
respect to a reference channel. Fig. 11 represents the
normalized correlation function after calibration when
the antenna array was perpendicular to the calibration
source.
[0075] High-resolution sub-space based AOA estimation
algorithms rely on an estimation of signal sub-space
(or equivalently noise sub-space). In the ideal case,
M-K smallest eigenvalues of the correlation matrix are
all the same and equal to 02. Based on this fact, the
signal/noise sub-space can be easily found. However, in
practice the correlation matrix is estimated by the
finite number of samples. In such a case, it is
probably that all eignevalues of correlation matrix
become different such that it is difficult to
distinguish the dimensionality of signal sub-space.
Simulation results have revealed the importance of
correct signal subspace size estimation. A principle
based on the Akaike's Information Criterion (AIC) may
be used to estimate signal subspace size.
[0076] It should be noted that the AOA estimation can be
improved by using a gyro or rate gyro to monitor the
direction of the position vector between the two
antennas of the receiver (the doublet). The output of
the gyro could be used as an input to a compensating
process that allows for small deviations in direction.
The gyro output could also be used in a feedback to the
user as to the quality of the trajectory (stability of
the antenna orientation during the trajectory).
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Finally, the output of the gyro can be used as an input
to a mechanical stabilizing device that would maintain
the directional vector between the two antennas for the
duration of the trajectory.
[0077] As one aspect of the invention involves spatially
translating the antenna (or antennas) through a
trajectory, a velocity sensor may be used as a sensor
to provide an approximate indication of the trajectory
velocity with time. Such a sensor could be realized
based on MEMS accelerometers as mentioned above. The
velocity sensor together with the single antenna
provides necessary input to the processing to achieve
the desired performance.
[0078] It should be noted that any useful data processing
means may be used with the invention. As such, ASICs,
FPGAs, general purpose CPUs, and other data processing
devices may be used, either as dedicated processors for
the calculations or as general purpose processors for a
device incorporating the invention. The invention may
be used to enhance currently existing parameter
estimation hardware or software as the invention seeks
to provide statistical variety to the samples used for
parameter estimation.
[0079] The method steps of the invention may be embodied in
sets of executable machine code stored in a variety of
formats such as object code or source code. Such code
is described generically herein as programming code, or
a computer program for simplification. Clearly, the
executable machine code may be integrated with the code
of other programs, implemented as subroutines, by
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external program calls or by other techniques as known
in the art.
[0080] The embodiments of the invention may be executed by a
computer processor or similar device programmed in the
manner of method steps, or may be executed by an
electronic system which is provided with means for
executing these steps. Similarly, an electronic memory
means such computer diskettes, CD-Roms, Random Access
Memory (RAM), Read Only Memory (ROM) or similar
computer software storage media known in the art, may
be programmed to execute such method steps. As well,
electronic signals representing these method steps may
also be transmitted via a communication network.
[0081] Embodiments of the invention may be implemented in any
conventional computer programming language For example,
preferred embodiments may be implemented in a
procedural programming language (e.g."C") or an object
oriented language (e.g."C++"). Alternative embodiments
of the invention may be implemented as pre-programmed
hardware elements, other related components, or as a
combination of hardware and software components.
[0082] Embodiments can be implemented as a computer program
product for use with a computer system. Such
implementations may include a series of computer
instructions fixed either on a tangible medium, such as
a computer readable medium (e.g., a diskette, CD-ROM,
ROM, or fixed disk) or transmittable to a computer
system, via a modem or other interface device, such as
a communications adapter connected to a network over a
medium. The medium may be either a tangible medium
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CA 02679673 2009-09-18
Attorney Docket No. 1004PO13CA01
(e.g., optical or electrical communications lines) or a
medium implemented with wireless techniques (e.g.,
microwave, infrared or other transmission techniques).
The series of computer instructions embodies all or
part of the functionality previously described herein.
Those skilled in the art should appreciate that such
computer instructions can be written in a number of
programming languages for use with many computer
architectures or operating systems. Furthermore, such
instructions may be stored in any memory device, such
as semiconductor, magnetic, optical or other memory
devices, and may be transmitted using any
communications technology, such as optical, infrared,
microwave, or other transmission technologies. It is
expected that such a computer program product may be
distributed as a removable medium with accompanying
printed or electronic documentation (e.g., shrink
wrapped software), preloaded with a computer system
(e.g., on system ROM or fixed disk), or distributed
from a server over the network (e.g., the Internet or
World Wide Web). Of course, some embodiments of the
invention may be implemented as a combination of both
software (e.g., a computer program product) and
hardware. Still other embodiments of the invention may
be implemented as entirely hardware, or entirely
software (e.g., a computer program product).
[0083] A person understanding this invention may now conceive
of alternative structures and embodiments or variations
of the above all of which are intended to fall within
the scope of the invention as defined in the claims
that follow.
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