An ultrasonic transducer array for velocity measurement in underwater vehicles
Transcript of An ultrasonic transducer array for velocity measurement in underwater vehicles
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Ultrasonics 42 (2004) 473–478
www.elsevier.com/locate/ultras
An ultrasonic transducer array for velocity measurementin underwater vehicles
P. Boltryk a, M. Hill a,*, A. Keary b, B. Phillips c, H. Robinson b, P. White a
a School of Engineering Sciences, Electromechanical Research Group, University of Southampton, Southampton SO17 1BJ, UKb H Scientific Ltd., Unit 21 Somerset House, Hussar Court, Brambles Business Park, Waterlooville PO7 7SG, UK
c Chelsea Technologies Group, 55 Central Avenue, West Molesey, Surrey KT8 2QZ, UK
Abstract
A correlation velocity log (CVL) is an ultrasonic navigation aid for marine applications, in which velocity is estimated using an
acoustic transmitter and a receiver array. CVLs offer advantages over Doppler velocity logs (DVLs) in many autonomous
underwater vehicle (AUV) applications, since they can achieve high accuracy at low velocities even during hover manoeuvres. DVLs
require narrow beam widths, whilst ideal CVL transmitters have wide beam widths. This gives CVLs the potential to use lower
frequencies thus permitting operation in deeper water, reducing power requirements for the same depth, or allowing the use of
smaller transducers.
Moving patterns in the wavefronts across a 2D receiver array are detected by calculating correlation coefficients between bottom
reflections from consecutive transmitted pulses, across all combinations of receiver pairings. The position of the peak correlation
value, on a surface representing receiver-pairing separations, is proportional to the vessel’s displacement between pulses.
A CVL aimed primarily for AUVs has been developed. Its acoustical and signal processing design has been optimised through
sea trials and computer modelling of the sound field. This computer model is also used to predict how the distribution of the
correlation coefficients varies with distance from the peak position.
Current work seeks to increase the resolution of the peak estimate using surface fitting methods. Numerical simulations suggest
that peak estimation methods significantly improve system precision when compared with simply identifying the position of the
maximum correlation coefficient in the dataset. The peak position may be estimated by fitting a quadratic model to the measured
data using least squares or maximum likelihood estimation. Alternatively, radial basis functions and Gaussian processes successfully
predict the peak position despite variation between individual correlation datasets.
This paper summarises the CVL’s main acoustical features and signal processing techniques and includes results of sea trials
using the device.
� 2004 Elsevier B.V. All rights reserved.
Keywords: Correlation velocity log; Doppler velocity log; Navigation; Peak finding
1. Introduction
Modern maritime vessels are equipped with inte-
grated navigation systems that provide precise and
accurate position and speed estimates. Motion sensors
and positional systems such as the satellite-based globalpositioning system (GPS) are interlinked to provide real-
time navigation information.
In some circumstances, however, GPS is unavailable
for continuous data updates. For submerged vehicles
*Corresponding author. Tel.: +44-23-8059-3075; fax: +44-23-8059-
3053.
E-mail address: [email protected] (M. Hill).
0041-624X/$ - see front matter � 2004 Elsevier B.V. All rights reserved.doi:10.1016/j.ultras.2003.12.036
such as autonomous underwater vehicles (AUVs) the
water column acts as a Faraday cage and the onboard
navigation system cannot receive the electromagnetic
GPS signals. In these conditions the AUV must either
surface or allow an antenna to breach the water surface
in order to receive the GPS information. This isimpossible when the water surface is, for example,
blocked by polar ice and may be unacceptable when
stealth is important or the AUV is operating at a deep
level.
AUVs frequently use inertial navigation systems
(INS) to measure accelerations. Velocity is estimated
through integration of the acceleration data and posi-
tion information achieved through a further integration
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Fig. 1. A typical two-dimensional transducer array configuration.
474 P. Boltryk et al. / Ultrasonics 42 (2004) 473–478
process. Whilst INS is extremely accurate in the short
term, it is not generally used alone for determining
velocity and position. In the longer term the reliability of
the estimates are affected by bias errors within the
integration calculations. To counteract this problem it isusual to employ a secondary instrument to routinely
zero the bias. Traditionally the well-established Doppler
velocity log (DVL) has been chosen as the secondary
instrument. However, an alternative ultrasonic-based
system called a correlation velocity log (CVL) has sig-
nificant advantages over DVLs for many AUV appli-
cations. This paper compares the characteristics of
DVLs and CVLs for use in typical AUV applications,and summarises the development of a CVL system de-
signed specifically for use in AUVs.
2. Navigation in AUVs
DVLs and CVLs represent two options from a range
of instruments that may be selected for navigating anAUV in conjunction with an INS system. The other
alternatives include mechanical flowmeters, pitot tubes
and electromagnetic speed logs. Since these systems
measure the speed of the vessel through the water their
accuracy may be affected by boundary layer effects and
corrections are necessary to compensate for crosscur-
rents in the seawater. In contrast, both DVLs and CVLs
can track the vehicle speed relative to fixed acousticscatterers on the seabed resulting in higher confidence in
the speed estimate because they reference the stationary
seabed [1].
DVLs have traditionally been the standard choice for
integration into AUVs; CVLs have not affected this
domination of the marketplace because of the high level
of signal processing required for correlating pairs of
signals in CVLs. However, the rise of DSP technologyhas meant that CVLs are now a realistic proposition
with the potential to offer improved velocity estimate
performance over a DVL. Whilst DVLs and CVLs both
operate by insonifying the seabed with ultrasonic signals
there are distinct differences between the operating
principles of the two instruments that significantly af-
fects their performance.
3. CVL operating principle
The specific operating principle of CVLs has been
described in detail elsewhere (see for example, [1–3]). In
its simplest form the CVL consists of an ultrasonic
transmitter and an array of receiving elements, a typical
example of which is shown in Fig. 1. Two short-durationultrasonic signals are transmitted downwards towards
the seabed, separated in time by a known interpulse
interval s. The pulses are reflected by acoustic scatterers
both on the seabed and suspended in the water column.
The waveform detected across the receiving array is
formed by the superposition of all the echo components
from the scatterers. The precise form of this acoustic
wavefront depends on factors such as the reflectivity of
the scatterers and their position in space relative to the
transmitter and receiver array. A CVL moving through
this sound field can therefore distinguish moving pat-terns in the acoustic signature detected across the re-
ceiver array.
There are two specific types of CVL, namely spatial
and temporal CVLs. In a spatial CVL the signals de-
tected by its receivers are compared with those signals
from the second pulse by calculating a correlation
coefficient between all combinations of receivers. Given
a known interpulse interval s the spatial CVL searchesfor the spatial vector D separating the receivers exhib-iting the maximum correlation between consecutive
pulses. In a prototype spatial CVL system described in
this paper, a velocity vector map is constructed by
plotting the spatial vectors separating all combinations
of receiver pairings. The CVL searches for the peak of
the surface generated by superimposing the correlation
coefficient data onto the corresponding velocity vectormap position. Since the interference patterns detected
across the receiving array move at twice the speed of the
CVL through the water, but in the opposite direction,
velocity is estimated using (1).
u ¼ � D2s
ð1Þ
A temporal CVL differs in that it adapts the interpulse
interval s to maximise the calculated correlation coeffi-cient between the acoustic signals detected on a pair of
receivers spaced at a known distance D.Whilst CVL systems detect similarities in the time
histories of received signals across a receiver array, theDVL detects the Doppler shift between transmitted
signals and the resulting backscatter from fixed acoustic
scatterers on the seabed as an indication of vehicle
velocity. To resolve directional information, four inde-
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P. Boltryk et al. / Ultrasonics 42 (2004) 473–478 475
pendent narrow beamwidth projectors are often ar-
ranged in a Janus configuration. The Janus configura-
tion arranges the four projectors to point towards the
seafloor in the forwards, aft, port and starboard direc-
tions; differences in the Doppler shift in the four direc-tions can thus be used to estimate velocity. To achieve
the necessary directionality, the ultrasonic transmitters
operate at a relatively high frequency, typically between
100 kHz and 1 MHz.
Fig. 2. Comparing the correlation coefficient distribution as a function
of distance from peak, upper plot using the empirical model to gen-
erate the predicted distribution and lower plot using actual trials data.
4. AUV applications
An important advantage of the CVL system is that
the velocity estimate is independent of sonic velocity
because it can be assumed that during the short inter-
pulse interval separating the pair of transmitted sig-
nals the ocean environment conditions remain constant.
The CVL therefore does not need to make tempera-
ture or salinity profiling measurements to maintain
precision.Whilst DVLs require narrow beams the ideal trans-
mitter for the CVL has a wide beam width, typically
about 60�. Reducing the directionality criterion awayfrom narrow beams allows for smaller transducers than
DVLs for the same operating frequency, or the potential
to use lower frequency operation for the same size of
transducer. This presents two significant advantages for
the application of CVLs to AUV platforms. Firstly,smaller and lighter transducers integrate more easily
with the existing AUV structure. Secondly, operation at
a lower frequency results in lower attenuation of the
acoustic signals. The CVL is either able to operate in
deeper water for the same transmitted power, or is able
to operate with lower power requirements than the DVL
operating in the same conditions. Both CVLs and DVLs
are able to extend their operating range in deeper waterby referencing waterborne scatterers rather than by
being limited to use fixed scatterers on the seabed.
However, this mode of operation is unfavourable be-
cause the velocity estimation is subject to current effects
and therefore the velocity estimate is not necessarily
velocity over ground.
The short-duration pulses transmitted into the water
by CVLs further reduce projector power requirements.Combined with a broadband acoustic signature, the
short pulses are less easily detected and therefore make
the CVL attractive for covert operation.
Another drawback with the DVL is that it requires
there to be a relative velocity between the acoustic tar-
gets and source. It is therefore not well suited to low
speeds or hover situations, which are common charac-
teristic flight profiles for AUVs. In contrast the CVLmaintains accuracy at low speeds and the prototype
system that has been developed is capable of operating
during hover manoeuvres.
5. Development of COVELIA
A spatial CVL system named COVELIA aimed spe-
cifically for AUVs has been developed. Its acoustical
and signal processing design has been optimised throughextensive sea trials and computer modelling of the sound
field.
5.1. Computer modelling and simulation
A detailed model, described in depth in Ref. [5],
investigated the characteristics of the sound field as de-
tected by receivers in the CVL array on a pulse-by-pulsebasis. This detailed model simulates a random distri-
bution of acoustic scatterers on the seabed, each scat-
terer acting as an acoustic source. Using in-depth
models of the acoustic reflectivity characteristics of the
scatterers, the attenuation of the signals through the
seawater and the sensitivity characteristics of the trans-
ducers based on transducer test data, the detailed model
predicts the waveform as detected by each receiver in agiven receiver array configuration. Thorough investiga-
tions into behaviour of the correlation coefficient dis-
tribution across the velocity vector map have developed
an empirical model that is used to predict the distribu-
tion of the correlation coefficient surface as a function of
the distance from the peak position on the velocity
vector map. Parametric tests have investigated the effect
on this correlation coefficient surface distribution causedby changes in receiver array configuration, seabed con-
ditions, vertical velocity, background noise and trans-
ducer properties such as the Q-factor and centre
frequency.
Fig. 2 compares the correlation coefficient surface
distribution as predicted by the empirical model with the
distribution obtained using sea trials of the prototype
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476 P. Boltryk et al. / Ultrasonics 42 (2004) 473–478
system. Considering first the trials data in Fig. 2, the
magnitude of the correlation coefficients as calculated
for each receiver are plotted as dots as a function of the
distance from the estimated peak position for each sea
trials dataset. For the modelled data, a peak position isassigned manually at random throughout the velocity
vector map and correlation coefficients assigned to the
measurement points using a binomial distribution
between the limits as defined by the detailed model.
Calculating the absolute distances between the mea-
surement points and the corresponding peak position,
the magnitude of the correlation coefficients can be
plotted as a function of the distance from the peak po-sition. The distribution of the modelled correlation
coefficient data can be illustrated using a large number
of repeat tests. The modelled data here uses parameters
such as depth that are equivalent to the sea trials con-
ditions.
The detailed model has shown that it is preferable to
sample a specific window of the incoming acoustic
waveform rather than calculating correlation coefficientsbased on the entire time history. This correlation time
window (CTW) affects the definition and width of the
correlation coefficient surface’s peak. The earliest re-
turns resulting from specular reflections immediately
below the CVL are found to contain little spatial
information for the CVL. Referring to Fig. 3, progres-
sively delaying the CTW though the entire received
waveform tends to make the correlation peak narrower.However, this also causes the characteristic side lobes of
the correlation surface to become more prominent.
Increasing depth insonifies a wider annulus on the sea-
bed for a given beamwidth and reduces the variance on
the surface.
Fig. 3. The influence of CTW and depth on correlation coefficient
distribution as a function of distance from peak position in element
spacings. Upper three plots vary the CTW for a fixed depth of 40 m.
Lower plots vary depth with CTW set as 20–30% of complete time
history.
A macroscopic model [6] is used to simulate the
performance of COVELIA in the long term using
parameters found using the detailed model. The macro-
scopic model sets up a simulated test run around a user-
defined track, and the AUV is subjected to randomcrosscurrents and waves. Correlation coefficient data is
generated using the empirical model according to the
environmental conditions and the chosen CTW. The
model then predicts velocity and position using peak
finding routines on the simulated correlation data.
5.2. Acoustical development
COVELIA uses a two-dimensional receiving array
that facilitates velocity estimation throughout the for-
wards, backwards, starboard and port directions. It is
additionally possible to infer vertical velocity although
in practice it may be easier to calculate change in depth
using static pressure measurements. The Tonpiltz
transducers are housed in impedance matching oil and
operating at 60 kHz permits operation up to an altitudeof 500 m above the seabed. COVELIA’s 60 kHz oper-
ating frequency is a compromise between a deeper
propagation of the acoustic pulses due to the reduction
in attenuation with decreasing frequency whilst avoiding
the background noise at lower frequencies.
The acoustic power requirements have been mini-
mised to make integration into an AUV favourable. The
transmitted acoustic pulses are very short, typicallyabout 10 acoustic cycles at 60 kHz. Additionally, the
maximum repetition rate is intended to be only about 4
measurement cycles per second in shallow water. Since
the maximum repetition rate is dependent on the height
above the seafloor it is necessary to operate at a lower
repetition frequency in deeper water.
5.3. Onboard signal processing
To ensure that the incoming signals used within the
correlation calculation result from bottom reflections
rather than volume reverberation or background noise,
COVELIA first determines the height of the instrument
above the seabed and uses time of flight calculations to
neglect signals detected before this depth threshold. The
CVL can therefore operate as a depth sounder to verifyother onboard instrumentation. The transmitted power
is then automatically adjusted to take into account the
attenuation of the returns from the seabed caused by
transmission losses and absorption losses by the sea-
floor. After a predetermined post-trigger period, COV-
ELIA samples the incoming received waveforms for a
sample length equal to the required CTW.
The complex form of the correlation coefficient (2)between two complex acoustic signals v1ðtÞ and v2ðtÞ isused by COVELIA [4]. In usual notation, v�2ðtÞ is the
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Fig. 4. Comparison of raw COVELIA speed estimate versus Raystar
120 GPS speed prediction.
P. Boltryk et al. / Ultrasonics 42 (2004) 473–478 477
complex conjugate time history of the complex signal
v2ðtÞ.
q ¼Rþ1�1 v1ðtÞv�2ðtÞdt
Rþ1�1 jv1ðtÞj2 dt
Rþ1�1 jv2ðtÞj2 dt
h i1=2 ð2Þ
This allows phase information to be calculated and
the magnitude of the correlation coefficient is found
to be robust to the phase effects caused by vertical
velocity. A signing algorithm is used to improve the
clarity of the correlation surface. The phase of the cor-
relation data at a given velocity vector point is com-pared with the phase at the highest point in the
correlation coefficient dataset. If the difference in phase
exceeds 1=2p then the correlation coefficient magnitudeat that point is multiplied by )1. Referring to Fig. 3,rather than being bounded to lie within the range 0 to 1,
the signed correlation coefficient magnitude varies be-
tween )1 and +1.The spatial vectors separating all combinations of the
receivers are plotted onto a grid called a velocity vector
map. By superimposing the correlation coefficient data
onto this map, a correlation surface is produced whose
peak position represents the optimum estimate for the
separation vector D. The velocity estimate therefore re-quires COVELIA to estimate the peak position of the
surface using the available correlation data. The reso-
lution of the instrument is improved by using peakfinding methods. Various peak estimation methods have
been investigated [7], including those based on highest
point (HP), least squares (LS), and maximum likelihood
estimation (MLE). Current work concentrates on
exploiting the learning behaviour of Gaussian processes
(GP) [8] and radial basis functions (RBFs) [9] for peak
finding.
HP is the most rudimentary peak estimation method,whereby the indices of the highest point in the correla-
tion coefficient dataset are selected as the peak position.
Whilst this method provides no interpolation between
measurement points it has proven to be a reliable fall-
back method when more complicated methods fail. An
axisymmetric quadratic model is fitted to the correlation
coefficient data using both LS and MLE methods. The
correlation surface peak is estimated by finding the peakposition of the fitted model. MLE is implemented in a
non-linear iterative process, which results in weighted
LS, where the weighting function is the expected vari-
ance at the given measurement points. Both GP and
RBF models are trained offline using sample data, where
the inputs to the model are the correlation coefficients
and the output is the predicted peak position. The
trained GP and RBF models make peak predictions inreal-time, and a quality factor may be output from the
model, which is useful for integrating the output data
with other navigational devices using processes such as
Kalman filtering. Simultaneous models are used for
estimating the peak position in the x and y directions.LS is currently the favoured peak estimation method.
It achieves good results throughout the measurement
area, and avoids the iterative process necessary for MLEand the complexity of GPs and RBFs. Preliminary re-
sults suggest that GPs are extremely well suited to peak
finding in this context, although inclusion of this peak
estimation technique could affect the maximum repeti-
tion rate of the measurement cycle due to the additional
computational load over LS.
6. Trials data
COVELIA has undergone a comprehensive test pro-
gramme that has included tests in different conditions. A
reservoir environment has been used extensively, to-
gether with tests offshore. The trials data in this paper
represents a short period during a recent sea trial on a
test boat that is equipped with precision navigationalequipment including a Raystar 120 GPS system.
The peak finding algorithm used during these tests is
LS with HP fallback. Whilst COVELIA estimates
velocity directly, it is more useful here to compare its
performance with respect to the reference GPS system
by considering the speed and the position estimated by
each system. Referring to Fig. 4, the raw COVELIA
speed estimate tracks the GPS speed reliably, althoughthe COVELIA data has a significantly greater variation.
Note that whilst the GPS system uses filtering tech-
niques the COVELIA speed data presented here is
unfiltered. Fig. 5 plots position using GPS data versus
the estimated position using COVELIA over the same
period of about 36 min.
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Fig. 5. Comparison of position estimation using Raystar 120 GPS
system and COVELIA.
478 P. Boltryk et al. / Ultrasonics 42 (2004) 473–478
7. Conclusions
DVLs have traditionally been the instrument of
choice for interfacing with INS in the AUV navigation
system. An alternative ultrasonic-based navigational aid
that is aimed specifically for AUVs has been developed
and tested. Rather than detecting Doppler shifts in the
ultrasonic echoes from the seabed, the CVL searches forsimilarities in the signals detected across a receiver
array, and estimates velocity of the vessel based on the
spatial separation vector corresponding to maximum
correlation between receivers, and the time interval
separating a pair of ultrasonic pulses directed at the
seafloor.
COVELIA has been developed using a combination
of computer modelling of the sound field, and a com-prehensive sea trials programme. The computer model-
ling has been influential in the selection of design
parameters such as the optimum correlation time win-
dow. The empirical model of how the correlation surface
distribution varies with distance from peak position has
been used to test a variety of peak finding methods for
improving the precision of the instrument. Whilst LS
combined with capability to revert to highest point has
currently been selected for use in COVELIA, numerical
studies suggest that Gaussian processes offer promisingcapabilities for peak finding on the correlation data.
Sea trials data benchmarked against GPS demon-
strate that the prototype COVELIA is a reliable and
accurate instrument. Current work seeks to fine-tune the
RBF and GP models to make their inclusion into the
device more attractive.
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