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

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

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.

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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