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62 IEEE JOURNAL OF OCEANIC ENGINEERING, VOL. 25, NO. 1, JANUARY 2000

Communication Over Doppler Spread ChannelsPart I: Channel and Receiver PresentationTrym H. Eggen, Arthur B. Baggeroer, Fellow, IEEE, and James C. Preisig, Member, IEEE

AbstractScattering functions from several experimentsdemonstrate that acoustic underwater channels are doublyspread. Receivers used on these channels to date have difficultywith large Doppler spreads. A receiver to perform coherent com-munication over Doppler spread channels is presented in this firstpaper of two. The receiver contains a channel tracker and a lineardecoder. The tracker operates by means of a modified recursiveleast squares algorithm which makes use of frequency-domainfilters called Doppler lines. The decoder makes use of the channeltracker coefficients in order to perform minimum mean squareerror decoding. This first paper treats theory aspects whereas thesecond part presents implementation issues and results.

Index TermsAcoustic signal processing, adaptive systems, dig-

ital communication systems, Doppler spread, least squares.

I. INTRODUCTION

COMMUNICATION in the ocean by means of acousticsignals has been in use for several decades, and numeroussimulations have been carried out in the literature in order

to quantify capabilities and limitations. The attenuation of

acoustic waves is roughly proportional to the square of the

frequency [1], making the communication channel severely

bandlimited, which limits the achievable range. Communica-

tion channels may exhibit both dispersion in time (delay spread)

and frequency (Doppler spread); in this paper, we are partic-

ularly interested in the Doppler spread channels. Emphasisin early systems was on incoherent communication where

phase information of the signal is not used, and frequency shift

keying (FSK) is common [2], [3]. An overview of existing

configurations before 1984 can be found in [4]. During the last

eight or nine years, the feasibility of coherent communication

in the ocean has been demonstrated, but incoherent schemes

like MFSK are still widely used to gain robust communication.

The phase-coherent schemes require phase tracking of the

channel because the information is transmitted by means of

the signal phase. The most common modulation techniques

are phase shift keying (PSK) and quadrature PSK (QPSK).

Systems with this modulation are reported in [5][8]. With

this modulation, Doppler shift becomes an important issue.

One way to deal with this is to use adaptive delay taps and a

phase-locked loop (PLL).

Manuscript received October 17, 1999; revised November 4, 1999.T. H. Eggen is with Simrad AS, N-3191 Horten, Norway.A. B. Baggeroer is with the Massachusetts Institute of Technology, Cam-

bridge, MA 02139 USA.J. C. Preisig is with the Woods Hole Oceanographic Institution, Woods Hole,

MA 02543 USA.Publisher Item Identifier S 0364-9059(00)00994-8.

A large body of simulation studies is reported in many dif-

ferent periodicals and books. They cover all aspects of under-

water acoustic communication systems such as channel identi-

fication and tracking, coding, modulation techniques, and spa-

tial diversity combining. Simulations of acoustic channels with

emphasis on the communication aspect is given in [9] and [10]

and addresses the stability of the channel multipath and phase.

The multichannel receiver for both incoherent [11] and coherent

[6] communication is reported to give significant gain. These

involve both simulations and demonstrations in shallow-water

environments. The problem of optimally combining multiple

channels is also simulated in [12]. The combination of beam-forming and adaptive equalization is reported in [17] where ray

tracing is used to extract significant paths.

At the frequencies in question here, a ray representation of

the acoustic propagation is valid. A simple range-invariant ray

representation with a piecewise linear sound-speed profile is

used. The underwater communication channels are modeled

as linear time-variant (LTV) systems due to processes such

as tides, currents, moving transmitter/receiver/surface, and

internal waves. Thus, the receivers are often adaptive in order to

track the channel [13], [14], and a common adaptive algorithm

for this purpose is the recursive least squares (RLS) [ 15], [16].

We use theframework of a doubly spreadchannel [18] to clas-

sify underwater communication channels. This means that thereceived signal is dispersed in both time and frequency. Under-

water communication channels depend on sound propagation

conditions, channel geometry, and boundary conditions. Thus,

one encounters delay spread, doubly spread, and Doppler spread

channels. The results in this paper are in the scenarios where the

carrier is around 20 kHz with bit rates not exceeding 10 kbit/s

and a range of a few kilometers. Both shallow and deep-water

channels are considered.

The context of this paper is coherent communication using

QPSK modulation in channels that have more severe Doppler

spread than delay spread in the sense that common receivers

are able to compensate for the delay spread but not the Doppler

spread. This paper is part I of two papers treating this problem.We present the LTV channel model and measured channels in

Section II. The receiver makes use of frequency-domain filters

called Doppler lines presented in Section III. An adaptive re-

ceiver suited for doubly spread channels using a modified RLS

with a time update step is presented in Section IV. This re-

ceiver is specialized to the Doppler spread channel in Section

V. This section also contains a brief simulation example with

Doppler spread data comparing a decision feedback equalizer

(DFE) with the receiver presented herein. The work presented

in part I is summarized in Section VI. In part II [ 19], the results

0364-9059/00$10.00 2000 IEEE
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Fig. 1. 3 dB contours of the cross-ambiguity function from four differentunderwater communication channels. Upper left: Arctic Ocean. Upper right

and lower left: Newport, RI. Lower right: Bahama Islands.

TABLE ICHARACTERISTICS OF THE DIFFERENT

UNDERWATER COMMUNICATION CHANNELS

is heaving, yielding the Doppler spread, and the return around

2 ms is the direct path. The return around 4 ms is consistent with

the travel time for a surface bounce. In the lower left panel, the

suspended source is moving vertically due to significant heave

and roll of the surface vessel. The received signal level was ob-

served to be very sensitive to source depth. Thus, it is believedthat a sound channel is present, and this is also suggested by ac-

companying sound speed profiles taken at the location the same

day. In the lower right panel, the communication takes placebetween a surface vessel and a bottom-mounted receiver. The

energy around 4 ms is the direct path and the severely spread

cluster around 25 ms is believed to be a surface bounce. The

transmissions were carried out in sea state 45.

All transmissions use QPSK modulation. There is a large

variation in the channels: The upper left panel shows a delay

spread (LTI) channel, the right panels are doubly spread chan-

nels, and the lower left panel is a Doppler spread channel. The

characteristics in terms of delay and Doppler spread are so dif-

ferent that one can hardly hope for one particular communica-

tion system serving all these channels appropriately. The em-

phasis is on the channels similar to the lower left and upper

Fig. 2. The channel has scatterers at different ranges with different velocitiesso that the composite channel is time-variant.

right panels of Fig. 1, where the Doppler spread is a significant

problem.

B. Channel Model

The underwater communication channel is now modeled as

a linear time-variant system. Some of the sources of the time-variation are mentioned in Section I. Since QPSK modulation is

used, the transmitted symbols are

(8)

The modified delay-Doppler-spread function [20] is

our channel model where the modification is to allow the

delay-Doppler-spread function to be a slowly varying function

of time. The discrete representation of our channel is thus

(9)

where

output of the channel;

Delay-Doppler-spread function which is the

scattering amplitude at lag and Doppler

for time ;

input to the channel;

number of signal returns;

zero mean measurement noise with variance ;

energy in each symbol;

Doppler spacing.The notation in the index of the sum of (9) means that

there are pairs of ; there is no assumption on the distri-

bution of these points. Thus, the model contains doubly spread,pure delay spread, and pure Doppler spread channels that may

be sparse in both delay and Doppler. A physical interpretation

is shown in Fig. 2. This is a channel with scatterers at different

ranges moving with different velocities in order to give dif-

ferent Doppler shifts. Thus, the communication channel is time

varying from symbol to symbol.

The WSSUS assumption is broken when the delay-Doppler-

spread function is time varying. For this reason, we use a quasi-

WSSUS assumption allowing for time variation in the delay-

Doppler-spread function . Thus, the physical interpretation

given below is not in general valid but, as the following numbers

show, it is useful in our scenario. A typical symbol rate is 1000
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EGGEN et al.: COMMUNICAT ION OVER DOP PL ER SP RE AD CHANNEL S PART I: CHANNEL AND RE CEIVER P RES ENTATION 6 5

symbol/s so that a symbol extends 1500/1000 m = 1.5 m when

the sound speed is 1500 m/s. A Doppler shift of 5 Hz at 20 kHz

carrier means a scatterer speed of 0.38 m/s. Data are transmitted

in packets of typical length 2 s. The scatterer moves 0.38 2 m

= 0.76 m in this time. Thus, the scatterer is within one symbol

length (=1.5 m) during the entire transmission. This is the as-

sumption that makes the physical interpretation of Fig. 2 useful

and (9) valid. It should be used with care since it is obviouslynot satisfied for a higher Doppler, higher symbol rate, or longer

packet length. The time variation enters this scenario through

the motion of the scatterer.

It is noted that the channel model (9) is WSSUS only when

in (4) (in which case ). Therefore, the relation-

ships between the scattering function, cross-ambiguity function,

and delay-Doppler-spread function in (7) are true only in this

limiting case.

Now a state space description for a system, aiming at deriving

a recursive estimate of the state space vector, is defined as

... (10)

The state space model for is given by

. . .

(11)

where

. . .

(12)

and is a vector ofnoise values. The expression in (10) ismerely

a way to collect the characterization in a single variable

which is the input delay-spread function [20]. According to theAR(1) model (4)for thechannel evolution, we write thefirst part

of (11). The last expression of (11) is a rewriting of (9). The first

expression of (12) is a vector of transmitted symbols, and it is

called the observation vector . This may seem counterintu-

itive since are the transmitted, and not the observed,

data. However, in the state space description of (10)(12), the

unknown quantity is the channel and the receiver must es-

timate this quantity. The covariance of the system noise

is diagonal. The physical interpretation of this is that the scat-

tering processes at different (delay, Doppler) cells are uncorre-

lated. The quantity is the scattering strength at delay and

Doppler . The description in (11) and (12) is the basis for the

Fig. 3. The FFS Doppler line.

receiver that is presented in Section IV. In Section V, this re-

ceiver is specialized to Doppler spread channels. It contains a

time-dependent device called a Doppler line which is intro-

duced now.

III. DOPPLER LINES

The doubly spread underwater communication channel

exhibits both time- and frequency-dispersive fading which are

caused by the Doppler and delay spread of the medium and by

the transmit/receive platforms. Both delay spread and Doppler

spread are forms of dispersion, and there is a close connec-

tion between channels exhibiting delay spread and channels

exhibiting Doppler spread: If the time domain and frequency

domain are considered as dual domains, the delay and Doppler

spread channels may be thought of as duals. This concept of

duality is treated in depth in [27], and the reader should see this

reference for definitions and implications of duality.

The notion of a filter is often used for a device that makes

a weighted sum of differently delayed versions of a signal.

The name delay line is used for this device and Dopplerline for the device that makes a weighted sum of differently

Doppler-shifted versions of the signal. They are both filters

but in dual domains. We now look at some of the features

of Doppler lines, and connect them to their duals the more

frequently encountered delay lines. For this purpose, let us

assume a purely Doppler spread channel model with constant

. The channel model (9) with

yields

(13)

Note that the model (13) implies a time-variant channel. It is

therefore expected that the device needed to compensate this

channel is time-variant. Thus, the Doppler line is a time-variant

gain unlike its dual the delay line.

Consider the Doppler line in Fig. 3. The structure is similar to

that of a finite impulse response (FIR) delay line, and the only

difference is that the Doppler shift is used in place of the delay.

The boxes with the multipliers in Fig. 3 are mixers both sup-

plying the next mixer and the local weight with its output. This

structure is called the finite frequency spread (FFS) Doppler

line. The FFS is the dual of the FIR because, in the same way as

an FIR delay line has finite impulse response, as will be shown
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Fig. 4. The convolutionof thetwo signals yieldsthe outputof theFFS Dopplerline. Z ( k ) has period N so that ( k 0 k ) repeats.

below, its dual the FFS has finite frequency spread. The picture

in Fig. 3 is written as

(14)

and it corresponds to time-selective fading [28]. By taking the

discrete time discrete frequency Fourier transform, assuming

with being window length, one obtains

(15)

where and have period . Thus, the time-selective

fading results in a frequency spreading. The nonzero support ofis . If is a single frequency so that

for , the situation is as shown in

Fig. 4. It is found from (15) that

(16)

and, from Fig. 4, it is seen that no aliasing takes place if

. Thus, the single frequency has been spread on the

finite interval of width and thereby this Doppler line gets the

name FFS. The aliasing requirement constrains the bandwidth

of but for practical communication channels and signals

so that this constraint is not severe.

There is also a counterpart to an IIR filter called the infinitefrequency spread (IFS) Doppler line. It is given by

(17)

It is clear that, for the discrete signals used here, (17) inevitably

yields aliasing because may be nonzero for arbitrary large

. Thus, the second part of (17) is the motivation for the name

infinite frequency spread. This equation is the same as for

Fig. 5. Receiver built up of separate channel tracker and equalizer.

an IIR filter with as input and as output. Further

discussion of Doppler lines and their properties is found in [23].

IV. GENERAL RECEIVER

Both the channel and the data in our model (9)

are unknowns in a realistic situation. Thus, the receiver must

both track the channel and decode the data. The receiver archi-

tecture in Fig. 5 is motivated by this. It is built according to ademand for simultaneous channel tracking and coherent signal

combining to reduce dispersion in both time and frequency. In

order to achieve an initial estimate of the channel, a training se-

quence, which is a sequence of symbols that is known to the re-

ceiver, is used. In addition to the training sequence, a short syn-

chronization sequence is prepended to the unknown data. When

these symbols are processed, the receiver enters the tracking

mode. In this mode, it tracks the channel variation by using

previously decoded symbols as the channel input. The order

of operation of the receiver in Fig. 5 is Receive synchroniza-

tionTraining modeTracking mode. This approach as-sumes that the decoding is correct or else the channel input is

not known. When the decoding is incorrect, the estimation ofthe channel response degrades, and this in turn gives more in-

correctly decoded symbols. The impact of this effect is analyzed

in part II [19] of this paper.

A. Channel Tracker

The algorithm for channel tracking is a modified RLS with

a time update step yielding a recursive estimate of and a

recursive equation for the estimated error covariance of .

The regular RLS algorithm is given in [15], and it can be shown

that it minimizes a weighted sum of the errors

given by

(18)

The output of the decoder is the soft symbol estimate ,

and this is passed through a quantizer yielding the symbol es-

timate according to a minimum distance rule. In the case

of QPSK, the quantizer assigns to the closest of the four

valid symbols in (8). When the forgetting factor , the RLS

is identical with a Kalman filter [15], [16] for the system with

state space description

(19)
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provided that the inverse in this expression exists. Note the

connection to our channel model: the delay-Doppler-spread

function introduced in (9) is interpreted as the scattering

amplitude at whereas the recursive channel tracker in

the case of a purely Doppler spread channel is approximately

a sliding window Fourier transform as shown in (32). This

means that the tracked coefficients are the contribution to the

delay-Doppler-spread function within a frequency band givenby the Fourier transform resolution. This is the interpretation

of in this section.

B. Receiver Using IFS Doppler Lines

We now show that the minimum mean square error (MMSE)

receiver for a Doppler spread channel is the IFS Doppler line.

Given the signalmodel(27) and the estimateof in(30), the

transmitted data sequence is estimated by using the MMSE

criterion. T hus, and are assumed to be known,

and the task is to find . By the Gaussian noise assumption,the probability density for conditioned on and the set

of all is complex Gaussian and given by

(33)

The MMSE receiver for is obtained by minimizing the

exponent of (33) which yields

(34)

This amounts to dividing the current sample with a com-

plex gain and then choosing the closest symbol. It corresponds

to the IFS Doppler line of (17).

C. Receiver Using FFS Doppler Lines

There is nothing that prevents the denominator of (34) from

going arbitrarily close to zero. This is a potentialweakness remi-

niscent of the characteristic of a zero forcing equalizer [25]. One

way to introduce robustness in the zero forcing equalizer is to

constrain it to be an FIR filter. Motivated by this, the FFS-basedreceiver is introduced. Given , an FFS Doppler line can also

be used for decoding in a similar manner. In order to show this,

we use the MMSE criterion and, in addition, require the receiver

to be an FFS Doppler line as in (14). The coefficients of

this receiver are found by performing

(35)

where

(36)

Fig. 8. Decoding of a typical sparse underwater communication channel withmany symbol intervals between the returns and the returns at different Dopplershifts. The second return is from a surface swell with much longer period than

the packet length, so that a Doppler shift rather than a spread is the result.

In this case, the coefficients are given by [24]

(37)

for

(38)

Thus, the Doppler line is a central part of the receiver in the case

of a Doppler spread channel, and this is also true in the doublyspread case [24].

The emphasis in this work is on Doppler spread channels. The

Doppler lines are used in Section V. We show in Section V-B

that for a purely Doppler spread channel the MMSE receiver is

an IFS Doppler line. The FFS Doppler lines may also be used

as a more robust alternative, as shown in this section.

D. Simulation Example

Part II of this paper contains examples of decoding both sim-

ulated and real data from Doppler spread channels. In order to

further motivate the use of a Doppler-line-based receiver, a sim-

ulation example is included here in part I as well. A scenario issimulated where the channel response is constructed from two

rays with different Doppler shifts. It occurs as a result of trans-

mitter or receiver relative motion, and also when one of the rays

interacts with an ocean surface that has a long swell.

The upper left panel of Fig. 8 shows the resulting sparse com-

munication channel with different Doppler shifts on the two

widely spaced returns. The channel parameters are delays at (8,

28) ms and Doppler shifts at (0, 4) Hz. These parameters are the

outputs of a simulation of the sound field at the receiver using

raytrace. The physical parameters are summarized in Table II.

The three panels in Fig. 8 show the results; the 3-dB contours

of the ambiguity function (6) are shown in the upper left panel,
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TABLE IITHE PARAMETERS USED FOR SIMULATING THE ENVIRONMENT YIELDING THE

DOPPLER SPREAD DATA

Fig. 9. Performance of the DFE receiver with a PLL on a Doppler spreadsignal. The predicted symbols in the right column and the MSE decision errorin the right column for a two-path signal with, respectively, 0, 1, and 2 Hz

difference in Doppler between the paths in the upper, middle, and lower panels.

complex values of the TU-RLS taps as given by (21) in

the upper right panel, and the estimates of the decoded symbols

from (23) are plotted in the lower left panel. The SNR is

15 dB and the training sequence duration is 512 symbols. The

training sequence is used both to compute the cross-ambiguity

function and to achieve initial convergence of the TU-RLS. The

#taps, tracking is the channel tracker dimension of (9), the

#taps, inversion is the FIR filter order of (22), the SNR

is the ratio , lambda is the exponential weighting factor

of the TU-RLS (21) and # errors in is the ratio of trans-

mitted to erroneously decoded symbols. The TU-RLS containsonly two taps and the estimated symbols are based on a signal

combiner with eight taps. This scenario is the result when one

direct path and one surface-reflected path are present, and the

ocean surface has a swell with a period significantly longer than

the packet length.

The adaptive DFE with a PLL (see, e.g., [6]) is unable to

decode the case shown in Fig. 9 because thetotal Doppler spread

is too large. If there is no swell on the surface, the channel is LTI,

and the DFE decodes correctly. The right column of this figure

shows the predicted symbols for channels where the first return

is at 0 Hz as in Fig. 8 and the second return is at 0, 1, and 2 Hz

in the upper, middle, and lower panels, respectively. The left

column shows the MSE in the predictions. This example shows

decoding for a case where the Doppler spread contains discrete

tones,i.e., a numberof Doppler shiftsat differentdelays. Results

with continuous Doppler spread, discrete Doppler spread at the

same delay, and real data are presented in part II of this paper.

VI. CONCLUSION

Underwater communication channels are modeled as LTV

systems. The delay-Doppler-spread function is used to obtain

a model of the channels in this work. The scattering function

is used for channel characterization, and it is estimated from

the cross-ambiguity function. The channels obtained from real

data at various sites show significant dispersion in both time and

frequency. The receiver suggested for some of these channels

consists of a channel tracker, linear decoder, and quantizer; a

modified RLS algorithm that is used in the channel tracker is

presented. The most important modification is that the Doppler

is allowed to be different on each tap, and this enables effi-

cient tracking of Doppler spread. The receiver is constructed

to allow for the demodulation of signals which pass throughdoubly spread channels. In this paper, the concentration is on

Doppler spread channels. The concept of Doppler lines, which

are frequency-domain filters, is presented and used in the re-

ceiver. Part II of this paper contains results from using this re-

ceiver on real data as well as performance analysis.

ACKNOWLEDGMENT

The authors thank J. Catipovic, M. Johnson, and D. Nagle

for the use of some of the data in Fig. 1. Work carried out in the

MIT-WHOI joint program.

REFERENCES

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[3] J. Catipovic, M. Deffenbaugh, L. Freitag, and D. Frye, An acoustictelemetry system for deep ocean mooring data acquisition and control,in Proc. Oceans89, October 1989, pp. 887892.

[4] A. B. Baggeroer, Acoustic telemetryAn overview, IEEE J. OceanEng., vol. OE-9, pp. 229235, Oct. 1984.

[5] D. Thompson et al., Performance of coherent PSK receivers usingadaptive combining, beamforming and equalization in 50 km under-water acoustic channels, in Proc. Oceans96, 1996.

[6] M. Stoianovic, J. Catipovic, and J. Proakis, Phase-coherentdigital com-munications for underwater acoustic channels, IEEE J. Ocean Eng.,vol. 19, pp. 100111, Jan. 1994.

[7] J. A. Neasham et al., Combined equalization and beamforming toachieve 20 kbits/s acoustic telemetry for ROVs, in Proc. Oceans96,1996.

[8] V. Capellano, G. Loubet, and G. Jourdain, Adaptive multichannelequalizer for underwater communication, in Proc. Oceans96, 1996.

[9] T. Duda, Modeling weak fluctuations of undersea telemetry signals,IEEE J. Ocean Eng., vol. 16, pp. 312, Jan. 1991.

[10] A. Falahati, Underwater acoustic channel models for 4800 b/s QPSKsignals, IEEE J. Ocean Eng., vol. 16, pp. 1221, Jan. 1991.

[11] J. Catipovic and L. Freitag, Spatial diversity processing for underwateracoustic telemetry, IEEE J. Ocean Eng., vol. 16, Jan. 1991.

[12] Q. Wen and J. Ritcey, Spatial diversity equalization applied to under-water communications, IEEE J. Ocean Eng., vol. 19, Apr. 1994.

[13] J. Bragard and G. Jourdain, Adaptive equalization for underwater datatransmission, in ICASSP, 1989, p. 1171.

[14] J. F. Doherty, Decision feedback equalization of data with spectralnulls, in Military Communication Conf., September 1990.
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[15] L. Ljung,System Identification Theory for the User. EnglewoodCliffs,NJ: Prentice-Hall, 1987.

[16] S. Haykin, Adaptive Filter Theory. Englewood Cliffs, NJ: Prentice-Hall, 1996.

[17] G. Sandsmark, Simulations of an adaptive equalizer applied to highspeed ocean acoustic data transmission, IEEE J. Ocean Eng., vol. 16,Jan. 1991.

[18] H. L. van Trees, Detection, Estimation and Modulation Theory. NewYork: Wiley, 1968, pt. 111.

[19] T. H. Eggen, A. B. Baggeroer, and J.C. Preisig, Communication overDoppler spread channelsPart II: Receiver characterization and prac-tical results,, unpublished.

[20] P. A. Bello,Characterization of randomlytime-variant linearchannels,IEEE Trans.Inform. Theory, pp. 360393, Dec. 1963.

[21] T. Kailath, Sampling models for linear time-variant filters,, TechnicalReport, MIT Research Lab, 1959.

[22] L. A. Zadeh, Frequency analysis of variable networks, in Proc. IRE,March 1950, pp. 291299.

[23] R. Steele, Mobile Radio Communications: Wheatons Ltd., 1992.[24] T. H. Eggen, Underwater acoustic communication over Doppler spread

channels, Ph.D. dissertation, Massachussetts Institute of Technology,Cambridge, 1997.

[25] J. G. Proakis, Digital Communications. New York: McGraw-Hill,1995.

[26] A. W. Rihaczek, Principles of High Resolution Radar. New York: Mc-Graw-Hill, 1969.

[27] P. A. Bello, Time-frequency duality, IEEE Trans. Inform. Theory, pp.1833, Jan. 1964.[28] R. S. Kennedy, Fading Dispersive Communication Channels. New

York, NY: Wiley, 1969.

Trym H. Eggen was born in Oslo, Norway, on May 14, 1963. He receivedthe M.S. degree in electrical engineering from the Norwegian Institute of Tech-nology in 1987 and the Ph.D. degree from the Massachusetts Institute of Tech-nology, Cambridge, in 1997.

He is currently with the signal processing group at Simrad AS, Horten,Norway. His research interests are in the areas of communications, arrayprocessing, and adaptive algorithms.

Arthur B. Baggeroer (S62M68SM87F89)received the B.S.E.E. degree from Purdue University,West Lafayette, IN, in 1963 and the Sc.D. degreefrom the Massachusetts Institute of Technology(MIT), Cambridge, in 1968.

He is currently Ford Professor of Engineering andthe Secretary of the Navy/Chief of Operations Chairfor Ocean Science in the Departments of Ocean En-gineering, and Electrical Engineering and Computer

Science at MIT.

James C. Preisig (S87M91) received the B.S. degree in electrical engi-neering from theU.S. Coast GuardAcademy in 1980, theS.M. and E.E. degreesin electrical engineering from the Massachusetts Institute of Technology (MIT),Cambridge, in 1988, and the Ph.D. degree in electrical and ocean engineeringfrom the MIT/Woods Hole Oceanographic Institution (WHOI) Joint Programin Oceanography and Oceanographic Engineering, Woods Hole, MA, in 1992.

Prior to joining WHOI as a Scientist, he was a Visiting Assistant Professor inthe Department of Electrical and Computer Engineeringat Northeastern Univer-sity, Boston, MA, and a Visiting Investigator at WHOI. His research skills arein adaptive signal processing, signal propagation modeling, and numerical opti-mization.He currentlyappliestheseskillsin three researchprograms. Thefirst isthe development of a better understanding of the effect that environmental fluc-tuations have on propagation acoustic and electromagnetic signals, the second isto use this understanding to develop adaptive signal processing algorithms withimproved performance characteristics, and the third area is the development ofcomputationally robust and numerically efficient techniques for implementingnew adaptive algorithms.

Dr. Preisigis a memberof theSensor Array Processing Technical Committee.

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