Issues in Acoustic Signal — Image Processing and Recognition

331 Pages·1983·13.06 MB·English

by  Dr. J. C. Hassab (auth.)

Issues in Acoustic Signal -
Image Processing and Recognition
NATO ASI Series
(Advanced Science Institutes Series)
A series presenting the results of activities sponsored by the NA TO Science Committee,
which aims at the dessemination of advanced scientific and technological knowledge,
with a view to strengthening links between scientific communities.
The Series is published by an international board of publishers in conjunction with the
NATO Scientific Affairs Division
A Life Sciences Plenurn Publishing Corporation
B Physics London and New York
C Mathematical and D. Reidel Publishing Company
Physical Sciences Dordrecht and Boston
o
Behavioural and Martinus Nijhoff Publishers
Social Sciences The Hague, The Netherlands
E Applied Sciences
F Computer and Springer~Verlag
Systems Sciences Berlin Heidelberg New York
G Ecological Sciences
Series F: Computer and System Sciences No.1
Issues in Acoustic Signal -
Image Processing and Recognition
Edited by
C.H.Chen
Springer-Verlag Berlin Heidelberg New York Tokyo 1983
Proceedings of the NATO Advanced Research Workshop on Issues in Acoustic Signal
. Image Processing and Recognition
Professor Dr. C. H. Chen
Southeastern Massachusetts University, Department of Electrical and Computer
Engineering, North Dartmouth, MA 02747, USA
Published in corporation with NATO Scientific Affaires Division
ISBN-13:978-3-642-82004-5 e-ISBN-13:978-3-642-82002-1
DO I: 10.1007/978-3-642-82002-1
Library of Congress Cataloging in Publication Data.
Issues in acoustic signal-Image processing and recognition. (NATO ASI series. Series F, Computer and system
sciences; no. 1)
"Proceedings of the NATO Advanced Workshop Research on Issues in Acoustic Signal-Image Processing and
Recognition" - 1. p. verso.
"Held August 5-9, 1982 ... in San Miniato, Italy" - Pre!.
"Published in cooperation with NATO Scientific Affaires Division."
1. Underwater acoustics-Congresses. 2. Image processing -Congresses. 3. Signal processing - Congresses.
I. Chen, C. H. (Chi-hua), 1937. II. North Atlantic Treaty Organization. Scientific Affairs Division. III. NATO Advanced
Study Institute on Issues in Acoustic Signal-Image Processing and Recognition (1982: San Miniato, Italy). IV. Series.
QC242.177. 1983. 620.2'5. 83-547
ISBN-13,978-3-642-82004-5 (U.S.)
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned,
specifically those of translating, reprinting, re-use of illustrations, broadcastings, reproduction by photocopying
machine or sirnilar means, and storage in data banks. Under § 54 of the German Copyright Law where copies are
made for other than private use, a fee is payable to "Verwertungsgesellschaft Wort", Munich.
© Springer-Verlag Berlin Heidelberg 1983
Softcover reprint of the hardcover 1st edititon 1983
2145/3140-543210
Preface
The NATO Advanced Research Workshop on Issues in Acoustic Signal/Image Processing
and Recognition was held August 5-9, 1982 at the Cappuccini complex in San Miniato
Italy. The Workshop was primarily concerned with the underwater acoustic signal
processing and seismic signal analysis and a major effort was made to link these
topics with pattern recognition, i~ge processing and artificial intelligence.
Major issues and new approaches in these interrelated areas were closely examined in
the Workshop. In addition to paper presentations three discussion sessions were
held on! (1) spectral analysis in underwater acoustics, (2) seismic wave propaga
tion, seismic imaging and migration, and seismic inversion, and (3) unresolved issues
and future directions.
This Proceedings volume includes most presentations made at the Workshop. The
publication, like the meeting itself, is unique in the sense that it provides exten
sive interactions among the closely related areas stated above. Such interactions
which usually result in the integration of different systems or approaches are
certainly much needed to achieve some performance breakthrough while individual
systems or approaches reach their performance limit.
I am grateful to all participants for their active participation that makes the
Workshop very productive, and to Dr. Lewis J. Lloyd and Dr. Ralph Goodman for their
help to arrange an informative visit to the SACLANT ASW Research Centre for the
Workshop participants. I am confident that this publication will be equally produc
tive to report important current research results and near-future research activity
particularly in underwater acoustic signal processing.
C.H. Chen
Workshop Director
Table of Contents
Preface
by C.R. Chen
OVERVIEW
1. J.C. Rassab, "A Common Framework for Acoustic Signal Analysis
in the Ocean Environment" •.....••.•.•.•••..•..•...•..•.•••.•••..•.•.•..•..
PATTERN RECOGNITION PROCESSING
2. C.R. Chen, "Application of Signal Processing and Pattern Recognition
to Underwater Acoustics" • •• . ••• • . • . • . • . . . . . .• . . • . • . . • • . . • . • . . • . • .. • . • . . . . 35
3. J.M. Hovem and R.M. Holt, "Acoustical Classification of the Sea Floor" ••.. 77
ARTIFICIAL INTELLIGENCE APPROACH
4. J.N. Maksym, A.J. Bonner, C.A. Dent and G.L. Hemphill, "Machine Analysis
of Acoustical Signals" ...•.•••.••.•.•..••..•.•....•.•.••.•..•...•.••.•••.• 95
ISSUES IN ARRAY PROCESSING AND TARGET MOTION ANALYSIS
5. R. Klemm, "Problems of Spatial Signal Processing in Shallow Water" . .....•• 113
6. J .M. F. Moura, "Geometric Aspects in Array Processing" .••...•.•.....•..••.. 139
7. R.L. Moose, "Adaptive Range Tracking of Underwater Maneuvering Targets
Using Passive Measurements" •...•..•....•.......•.•.••.•.•...••.••.•.•...•. 155
8. M.J. Hinich, "Bearing Estimation Using Random Arrays" ..•••..•.•..••.•..•. 173
9. H. Gautier and P. Tournois, "How VHF Acoustics Can Help Process
Underwater Acoustic Signals" .•.•••.•.••.•....•...•.•.....•..••.•.•.....•.. 181
UNDERWATER CHANNEL CHARACTERIZATION
10. G. Jourdain, "Characterization of the Underwater Channel Application
to Communication" •••....••••...•.•...•..•.•..•..••.•.•...........•....•..• 197
ISSUES IN SEISMIC SIGNAL PROCESSING
11. G. Garibotto, "Multidimensional Filtering of Acoustic Records in
Seismic Signal Processing" •..•••.•..••..•.••••.•.•.•....•.••...•.••..••••• 211
12. J. Morlet, "Sampling Theory and Wave Propagation" •....•.•.••.•••••..•..... 233
IMAGE PROCESSING
13. T.S. Durrani, "Implementation of Image Enhancement and Least Squares
Filtering Techniques for Acoustic Data" .••.•.....•....•.•..•.•••••.....••• 263
14. V. Cappellini and A. Del Bimbo, "Digital Processing of Time Varying
Images" ..•..•.•.•..•..•.......•.••.•...••••.••.•.•.•••...•...••...••••..•. 283
15. J. Kittler, "The Template Edge Detector and Its Performance" •••.•..•.•..•• 295
16. C.H. Chen, "On a Spatial Clustering Algorithm for Image Analysis" •.•.•••.• 309
VIII
17. C. Braccini. G. Gambardella and A. Grattarola. "Digital Image 315
Processing by Means of Generalized Scale-Invariant Filters"
REPORT OF DISCUSSION SESSION ON UNRESOLVED ISSUES AND FUTURE DIRECTIONS 331
LIST OF PARTICIPANTS 333
A COMMON FRAMEWORK FOR ACOUSTIC SIGNAL ANALYSIS
IN THE OCEAN ENVIRONMENT
Dr. J. C. Hassab
Naval Underwater Systems Center
Newport, Rhode Island 02840
ABSTRACT. Estimates of arrival time intervals and their variation in time
constitute basic measurement in a signal processor for undersea applications. Those
measurements are then mapped in the data processor into the desired estimate of
emitting source range, direction, depth and velocity. The integration of the signal
and data processing stages within the signal analysis function (see Fig. 1) form the
subject of this paper. Figure 2 gives the three basic types of channels with the
pertinent time intervals and processing functions. The processor structure has a
spectral type formulation for its stationary elements and a state space formulation
for its nonstationary ones.
Though varied, the basic signal processing elements have a common set of issues
that can be addressed simultaneously (Fig. 3,4). Each signal processing stage has a
set of linear and non linear operations, Le. Fourier transform, absolute value,
log, tan-1• •• that map the embedded time interval in the input function into a
recognizable clue denoting the value of that time interval. The clue may be a
dominant peak or a slope. To enhance a deteriorating clue in the presence of varied
interferences, windowing has been incorporated into the basic signal proceSSing
stage. Gating is added to limit the search for the clue to the most probable region
in the signal processor output. The detected time intervals could then be passed
through a linear weighed least square filter to improve and assess the quality of
the estimates. Those estimates are fed back in a loose couple to adapt the
parameters of the gate and in turn those of the window, for instance. The same
estimates are also fed forward through a linearized or nonlinear weighted least
square filter to map the time interval estimates into smoothed source position and
velocity estimates (Fig. 5). The source parameter estimates are also fed back for
use in adaptive design of gate, window •••
An interactive system is depicted in Figure (6) that utilizes to best advantage
all available information in a synergistic way. The motivation for the described
approach lies in the desire to accommodate the widest variety of practical
situations with a priori information generally lacking. Too often sUb-systems are
NATO ASI Series, Vol. FI
Issues in Acoustic Signalllmage Processing and Recognition
Edited by C. H. Chen
© Springer.ve,lag Berlin Heidelberg 1983
2
compartmented and there is little or haphazard communication from one to another.
Each sub-system by itself has natural performance limits which can be enhanced
through sUb-system interactions ~ The coupling provides guidance for localized
search and adaptation, not direction to the local processor.
Directive information may lead to system instability.
1. INTRODUCTION
This paper addresses the problem of estimating source location and velocity
through passive observations of the source signal. Active observation of a
scatterer with the a-priori information on the probing signal is considered as a
subset of the passive observation problem. The total gain in a localization and
motion analysis system is derived from spatial or array gain (size, sensors number
and placement etc.), from inclusion of propagation channel features and from
temporal or processing gain (signal, data). More attention by system designers
should be drawn to the simultaneous optimization along both spatial and temporal
dimensions.
Practical considerations such as array stabilization, cost, spatial coherence,
platform dimension limit the achievable spatial gain, while the temporal
dimension is limited by the requirement to yield an acceptable solution wi thin a
given time lapse. When the elements of the problem are stationary, the temporal
processing reduces to a continual integration over the observed source clues until
desired accuracy in the solution is obtained. When the elements of the problem are
nonstationary i.e. moving source/receiver, changing channel characteristics ••• the
source observation is limited to a basic observation time interval, over which the
process may be considered locally stationary. The system gives then short memory
estimates i.e. its lowest level of performance, and yields a fuzzy idea on the
location and motion of the source. With the succession of basic time observation
intervals, the temporal processor basically removes the non-stationarity in the
problem and superimposes the repeated fuzzy pictures to reinforce the invariant
parameters in the problem and to develop a well-defined picture of source location
and motion.
In its totality, the estimation process constitutes a non-linear and
non-stationary problem that is not amenable to optimum global system synthesis. The
state-of-the-art would allow for subsystem (local) optimization and for integration
into a self-adjusting/self-organizing global system that achieves the desired
performance. The overall design guideline would be a robust performance under the
v'a,ried realistic conditions that the system is expected to encounter. Those
conditions should accommodate non-gaussian noise, non-stationary data due to
3
relative source/receiver motion or to varying signal/noise characteristics, channel
distortion and dispersion, sensor position instabilities, off-broadside source
directions, recursive short observation times of source, tonals in otherwise
broadband signals, interference. lfuen the conditions permit ideali zation, i.e.,
gaussian signal and noise, very long observation time of source, stationary
source/receiver, the system should self-aejust to yield a performance that
approximates the optimal performance criterion given by the Cramer-Rao bound.
This paper is not intended to be a list of contributors and contributions. It
is an attempt at merger of those contributions into a design assessment and. into
candidate structures. Hopefully it serves to highlight a system design approach in
underwater acoustics. Section (2) describes the elements of the signal, the basic
types of channels through which the signal would propagate, and the appropriate
processors to the encountered signals and channels. Section (3) discusses two
multiplicati ve operations common to all cases. 11indowing is executed in the
frequency domain and gating in the time domain. Section (4) assesses the various
processing schemes appropriate to channels with intersecting ray paths. Section (5)
evaluates the processors for non-intersecting ray path channels. Section (6) merges
the varied time delay measurements into source position and velocity estimates.
Section (7) formalizes the discussions in the preceding sections and presents an
interactive source tracking approach. Section (8) contains summary comments.
2. SOURCE, CHANNEL, PROCESSOR
Figure (1) describes a basic system and its function. The source signature may
include wideband, narrowband and transient elements. The signature traverses
non-intersecting or single paths and/or intersecting or multipath. The channel may
be distortionless or display distortion, dispersion and may support noise and
interference. The receiving sensors, after beamforming, are effectively
concentrated into a single point or spatially distributed points. The traditional
signal analysis of underwater systems have separated the signal from the data
processing functions and have marginally included channel characteristics in the
analysis process.
The two basic ocean channels are viewed, that of horizontal and of mul tipath
propagation. For each type of channel, recent signal and data processing methods
have been evaluated analytically and experimentally, then contrasted to existing
techniques. The traditional separation in the design of signal and data processing
should be replaced by an integrated approach that can lead to a measurable