iXblue - DELPH Sonar advanced notes

DELPH Sonar – Advanced Notes
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MU-DSOAN-AN-001 Ed. C – October 2013 i

Overview of the DELPH Sonar Advanced Notes
This document is the DELPH Sonar Advanced Notes. The DELPH Sonar Advanced Notes
document is divided into two parts:
• Part 1 – Side-Scan Sonar Basics: This first part contains a general presentation of a
side-scan imagery system.
• Part 2 – Operating the Software: This second part describes the step-by-step proce-
dure to operate the DELPH software
A Table of Contents is available in the following pages to allow quick access to dedicated
information.
MU-DSOAN-AN-001 Ed. C – October 2013 ii

Table of Contents
I SIDE-SCAN SONAR BASICS ........................................................................................ 1
I.1 Side-Scan Sonar Imagery System Presentation............................................................... 1
I.2 Side-Scan Sonar Principle................................................................................................. 2
I.2.1 Sensor Geometry ............................................................................................................ 2
I.2.2 Temporal Resolution ....................................................................................................... 5
I.2.3 Propagation..................................................................................................................... 6
I.2.3.1 Sonar Equation................................................................................................................ 6
I.2.3.2 Sound Velocity Model...................................................................................................... 7
I.2.3.3 Absorption and Propagation Loss.................................................................................... 8
I.2.3.4 Target Strength ............................................................................................................. 10
I.2.3.5 Ambient Noise............................................................................................................... 10
I.2.3.6 Contrast versus Range.................................................................................................. 11
I.3 Side-Scan Image Resolution and Range......................................................................... 12
I.4 Coverage Rate.................................................................................................................. 16
I.5 Sonar Data Acquisition.................................................................................................... 18
I.6 Sonar Positioning............................................................................................................. 19
I.7 Sonar Data Processing and Interpretation...................................................................... 21
I.7.1 Introduction ................................................................................................................... 21
I.7.2 Low Level Processing.................................................................................................... 22
I.7.3 Seafloor Detection......................................................................................................... 22
I.7.4 Radiometric Correction.................................................................................................. 23
I.7.5 Sonar Image Geometric Correction: Image Mosaicking.................................................. 25
I.7.5.1 Slant Range Correction ................................................................................................. 25
I.7.5.2 Image Geo-referencing.................................................................................................. 26
I.7.6 Object Measurement (Width/Length/Height, Position) .................................................... 27
II OPERATING THE SOFTWARE ......................................................................................28
II.1 Software Architecture ...................................................................................................... 28
II.2 Data Acquisition and Storage.......................................................................................... 29
II.2.1 Architecture................................................................................................................... 29
II.2.2 Main Important Features of Sonar Acquisition................................................................ 30
II.3 Data Processing and Interpretation................................................................................. 31
II.3.1 Automatic Bottom Detection .......................................................................................... 33
II.3.2 Radiometric Correction.................................................................................................. 34
II.3.2.1 Offset Correction Parameter.......................................................................................... 35
II.3.2.2 Time Varying Gain......................................................................................................... 37
II.3.3 AGC Correction............................................................................................................. 38
MU-DSOAN-AN-001 Ed. C – October 2013 iii

II.3.4 BAC Correction ..............................................................................................................39
II.4 Image Mosaicking .............................................................................................................41
IXBLUE CONTACT - SUPPORT 24/7 CUSTOMER SUPPORT HELPLINE ..................43
IXBLUE CONTACT - SALES .........................................................................................44
MU-DSOAN-AN-001 Ed. C – October 2013 iv

I SIDE-SCAN SONAR BASICS
I.1 Side-Scan Sonar Imagery System Presentation
Figure 1 – Side-Scan Sonar Imaging Flowchart
The main components of a side-scan sonar imagery system are shown in Figure 1:
• Step 1 - An acoustic sensor array with a positioning system
• Step 2 - Data acquisition and logging software
• Step 3 - Data processing and interpretation software
• Step 4 - A geographical information system (GIS)
The side-scan sensor produces acoustic images of the seafloor. It collects data along pa-
rallel lines. The acoustic signal is reflected by the seafloor when the towed fish is moving.
These raw acoustic signals are recorded simultaneously with positioning data (GPS,
USBL) using dedicated acquisition software. Following this, using the tools provided by
the processing and interpretation software, it is possible to analyze the acoustic image for
detection, classification and reporting purposes. The processed data (image mosaic, an-
notations, measurement, and contact analysis) can then be exported to any cartographic
GIS software to arrive at a full interpretation of the survey area in conjunction with other
kinds of data (magnetic, seismic profile, bathymetry, etc.).
MU-DSOAN-AN-001 Ed. C – October 2013 1

I.2 Side-Scan Sonar Principle
The acoustic emission is produced by a ceramic transducer that vibrates and resonates.
This transducer is stimulated by an input electrical signal. Symmetrically, on reception the
acoustic pressure vibration excites the ceramic and produces an electrical signal with an
amplitude proportional to the acoustic amplitude.
I.2.1 SENSOR GEOMETRY
The acoustic emission/reception sensitivity diagram, also called the beam pattern, de-
pends on the array geometry. For a rectangular array, the vertical hδθ and horizontal lδθ
beam width (defined at 3 dB attenuation) vary in a manner inversely proportional to trans-
ducer height H, length L and frequency f according to the following formula:
H
50λ
θ =h and
L
50λ
θ =l in degrees
where
f
c
=λ is the wavelength defined as the ratio of the sound velocity c and the mean
frequency. Beam patterns are shown in Figure 2. Typical values of angular resolution are
given in Table 1. This means that if the array shape is a rectangle elongated in one direc-
tion, it emits an acoustic beam in a plane perpendicular to that direction with a small hori-
zontal beam width and a large vertical beam. The intersection of this beam with the bot-
tom, called the footprint, is then a thin, nearly straight line. The shape of the footprint is in
fact a branch of a hyperbola approximated as a thin straight line over a short distance.
Table 1 – Angular Resolution versus Frequency
Length in m
Frequency in kHz
1.0 2.0
150 0.5 ° 0.25°
450 0.17° 0.08°
Figure 2 – Beam Pattern at 100 kHz and 400 kHz (Antenna Length 1 m)
Beam Pattern

The emitter sends a short modulated pulse (monochromatic or chirp). The acoustic vibra-
tion spreads and propagates to the seafloor. The main part of the acoustic vibration is re-
flected back to the fish after reaching the seafloor. The system then reemits a second
pulse once all the returns have been recorded. In a side-scan system, you select a “no-
minal” maximum slant range in meters that is internally converted to maximum time of
flight of the pulse and recording time on the basis of an average mean sound velocity.
Depending on fish height and slope and true sound velocity, the true slant range and
ground range will be different, and usually shorter (see Figure 3).
Figure 3 – Nominal Slant Range and True Ground Range
In the traditional side-scan configuration there are two arrays:
• one array for emission
• a second array for reception
The emission array has a length slightly smaller than the reception array. This pair of ar-
rays is mounted on the side of the fish with a tilt angle large enough to avoid any crosstalk
between echoes coming from the two sides of the vertical.
A second pair of arrays is mounted on the second side of the fish. The system creates two
bottom images simultaneously: one on the right (Starboard) and one on the left (Port). The
seafloor is not well illuminated directly under the fish (nadir) and resolution is also medio-
cre there. This zone (see Figure 4 and Figure 5) is called the blind zone and should be
taken into consideration when computing the true coverage of the system.
Slant Range
Two Arrays
Blind Zone

Figure 4 – Side-Scan Sonar Geometry: Rear View
Figure 5 – Side-Scan Sonar Geometry: Top View
In the side-scan geometry, the seafloor is “illuminated” by an inclined acoustic “light”,
which means that an object lying on the seafloor will appear as a strong echo accompa-
nied by an acoustic shadow. Figure 6 shows port and starboard side-scan images. The
horizontal axis is the slant range and the vertical is the along-track distance or ping axis.
The echoes are represented as bright pixels and shadows as black. The black area at the
centre is the acoustic noise signal from the water column.

Figure 6 – Side-Scan Sonar Image
I.2.2 TEMPORAL RESOLUTION
The pulse is either a monochromatic short pulse or a modulated signal characterized by
its bandwidth. The pulse duration T or the bandwidth B for a modulated emission will de-
fine the temporal resolution τ of the system as opposed to the spatial resolution defined by
the beam shape.
For a monochromatic emission, the temporal resolution is given by the pulse length:
τ = 1 / T
For a chirp-modulated emission, the resolution is the inverse of the bandwidth:
τ = 1 / B
The spatial resolution across the image track is directly related to the temporal resolution.
δx = cτ / 2
where c is the sound velocity.
For a typical values of τ ≈ 10 µs, we obtain δx = 7.5 cm.

I.2.3 PROPAGATION
I.2.3.1 Sonar Equation
The quality of the image does not depend solely on the spatial resolution but also on its
contrast, i.e. the ratio between the strength of the echo and its shadow (noise). This con-
trast is measured as the signal-to-noise ratio (SNR) achieved by the system. The SNR is
given by the well-known sonar equation for active systems, expressed in dB:
SNR = SL –2TL + TS – NL
• Where SL is the source level: transmitting power
• TL is transmission loss due to signal spread and absorption
• TS is target strength, the proportion of the signal reflected back by the target
• NL is the overall noise level that includes reverberation noise from surface, volume and
bottom, ambient and electronic noise NL = SRE + VRE + BRE + AN
Sound propagation, absorption and ambient noise effects are estimated using established
models - for instance:
• Chen & Millero for sound velocity
• Wenz model for ambient noise
• The Francois & Garrison model for absorption
• McKinney-Anderson for bottom reverberation
SNR for a given central frequency depends mainly on the range between the source and
the target. A detection system will for instance be specified so that the SNR is greater than
a detection threshold DT at a maximum range for a given resolution. Starting out from
these specifications, the design of the fish can be determined entirely by means of the so-
nar equation: frequency, height, width of the transducer, source level, etc. Figure 7 illu-
strates the various acoustic sources in the marine environment.
Figure 7 – Marine Environment

I.2.3.2 Sound Velocity Model
A typical sound velocity profile is shown in the Figure 8. A 10 m/s variation around a no-
minal value of 1500 m/s can be observed, corresponding to a maximum variation of 0.5%.
Figure 8 – A Typical Sound Velocity Profile
Sound velocity is mainly dependant on:
• Salinity
• Temperature
• Depth (pressure)
The consequence is that the acoustic rays are curved. Near the surface, the gradient
temperature can be so important that the acoustic rays may be reflected back to the sur-
face, creating a phantom image. The relationship is illustrated in Figure 9 using the Chen
& Millero model.
In side-scan imagery, sound velocity variation is often ignored and taken as a constant
mean value. The effect of variation of the sound in side-scan image is simply an overall
scale factor. For instance, for a variation of about 0.1% around the mean value, the mean
error for a range of 100 m is less than 10 cm. This is usually far less than other sources of
error (flat seabed assumption, positioning, heading error).

Figure 9 – Sound Velocity versus Depth (Chen & Millero Model)
I.2.3.3 Absorption and Propagation Loss
During propagation, vibration amplitude is attenuated by spreading and absorption. See
Figure 10.
• Acoustic loss due to propagation varies according to 1/R2
where R is the distance over
which the sound was propagated.
• Absorption loss decays exponentially, the overall loss TL is given in dB by:
RRlog20TL 10 α+=

Figure 10 - Transmission Loss versus Range
The absorption coefficient α depends on the frequency and water type (pH, salinity, tem-
perature, immersion). See Figure 11.
Figure 11 - Absorption Coefficient versus Frequency (Francois & Garrison Model)

I.2.3.4 Target Strength
The amplitude of the signal reflected back from a target TS depends on the nature of the
echoes and the grazing angle at which the signal hits the object. This index decreases
with frequency and increases with material density. Typical values for frequency around
100 - 200 kHz are shown in Table 2:
Table 2 - Target Strength for Typical Seabed Types
Type of bottom Target strength
Sand - 30 dB
Mud - 40 dB
Gravel - 20 dB
I.2.3.5 Ambient Noise
As shown with the Wenz model at frequencies of around 1 kHz -to 500 kHz, background
noise is dominated by surface noise. See Figure 12.
Figure 12 – Ambient Noise Level

I.2.3.6 Contrast versus Range
It is possible, using the sonar equation, to estimate SNR dependence on range and fre-
quency. In Figure 13, SNR is plotted at (150 kHz – 450 kHz) frequency interval and at (0
to 350 m) range interval. By setting a minimal detection threshold, this diagram gives the
maximum slant range for a given frequency.
Figure 13 – S/N Ratio versus Range
For example, at a frequency of 400 kHz, maximum range detection (for a 10 dB threshold)
is approximately 200 m but increases to 400 m at 150 kHz.

I.3 Side-Scan Image Resolution and Range
From the acoustic parameters defined above (amplitude, geometry, frequency, pulse
modulation), all the main geometrical characteristics of the side-scan image can be de-
duced: across- and along-track resolution, minimum and maximum range and image con-
trast.
Due to side-scan geometry, an object lying on the seafloor produces a high reflectivity
echo followed by a shadow zone. One of the most important components of the quality of
the sonar image is the contrast between echo and shadow levels. As seen in Figure 13,
contrast (like image quality) decreases with range. The effect of frequency on side-scan
range is shown in Figure 14. In practice, knowing the frequency of the sonar, the range
can be selected for a given contrast. Contrast can also be optimized by adjusting the
height of the sonar fish above the seafloor. Typically, it is recommended that fish height
should be around 15% of sonar range.
Figure 14 - Effect of Frequency on Image Contrast versus Range
Internally, the sonar range, defined in meters, is converted to a recording time for emis-
sion on the basis of an average sound velocity. The sonar emits a new pulse at the end of
recording and the range value therefore also defines the sonar pinging interval. For longer
ranges, this decreases the coverage rate (see I.4).
Contrast
Range

Some systems use a multiping emission mode to increase the pinging rate to overcome
this limitation but they do so at the expense of limiting the bandwidth.
The minimum range is defined by the minimum aperture angle. This minimum range also
defines the width of the blind zone at nadir.
The quality of the image is also dependant on its resolution. Resolution is defined as the
minimum distance between two echo points that can be discriminated in the image.
In the along-track distance, the resolution dδ is related to the horizontal beam hθ and va-
ries with the slant range distance R angle according to the following relationship:
hd R θδ *= which is minimum at the minimum range.
In the across-track direction, the resolution rδ is related to the temporal resolution accord-
ing to
( )g
r
c
θ
τ
δ
cos2
=
where c is the sound velocity and gθ is the grazing angle.
Resolution along- and across-track is illustrated in Figure 15, Figure 16, and Figure 17.
Figure 15 – Along-track Resolution
Figure 16 – Across-track Resolution (τ is constant)
Resolution

Figure 17 – Top View of Resolution Cell
At nadir, across-track resolution degrades rapidly. This means that even if the sonar beam
pattern illuminates the nadir, the image quality will be very poor. This is the reason why,
for a traditional side-scan fish, the beam pattern is tilted so the energy illuminates a region
where resolution will be good. Conversely, at distant ranges across-track resolution con-
verges rapidly to a constant. Along-track resolution is proportional to range, degrading ra-
pidly, and is the primary limiting factor. Since the sonar antenna cannot be very long (2 or
3 meters at most) due to physical limitations, a high-quality side-scan image is limited to
small range (typical < 300 m).
Note
This limitation does not apply to synthetic aperture sonar systems for which resolution
is independent of range.
Table 3 gives the resolution for an antenna length of 2 m, a frequency of 150 kHz and a
pulse length of 50 µs.
Table 3 - Along-track and Across-track Resolution
Range (m) Along-track resolution (m) Across-track resolution(m)
50 0.22 0.05
150 0.66 0.038
300 1.32 0.038
Figure 18 shows the effect of frequency on the side-scan resolution image. These data
were recorded using a dual-frequency sonar (100 and 400 kHz).
Nadir

Figure 18 – Impact of Acoustic Frequency on Image Resolution

I.4 Coverage Rate
Additionally, an important factor in choosing a sonar fish is optimization of survey time
versus resolution. Coverage rate CR is defined as the maximum surface area that can be
covered per hour. This is obtained as follows:
maxmax2 VRCR =
where Rmax is maximum ground range and Vmax the maximum fish speed.
In the definition given above, the coverage rate is NOT the full coverage rate since the
seafloor at nadir is not insonified. In order to achieve 100% coverage, it is necessary to
survey lines that overlap, in order to cover the gaps at nadir. This is usually achieved by
surveying a second set of lines overlapping the first set. See Figure 19 and Figure 20.This
will at least double the survey time:
maxmaxVRCRfull = (1)
One of the best strategies is to translate the second set of lines at ½ Rmax, giving 75%
overlap between two succeeding series of lines. Using that strategy the along-track reso-
lution δ will never be less than
4
3 hRθ
δ = .
It would be possible to increase the coverage rate by increasing fish speed but there is a
maximum admissible speed: the maximum speed is obtained when at the minimum range
the footprints of two successive emissions do not overlap. The maximum speed is then
given by:
max
max
2R
c
V
δ
= (2)
Combining Equations 1 and 2 above, the simple relationship giving the full coverage rate
is obtained as
2
c
CRfull
δ
=
Table 4 contains typical resolutions as examples.
Table 4 – Coverage Rate
Resolution (cm) Coverage Rate (km2
/h)
10 1
20 2
50 5

Figure 19 - Full Coverage Rate versus Resolution
Figure 20 – Survey Lines with 75% Overlap

I.5 Sonar Data Acquisition
On reception, the acoustic vibration creates an electrical signal with an amplitude propor-
tional to acoustic pressure. This signal is preamplified by applying an analog gain (either
automatic (AGC) or fixed (TVG)) before digitization. For digital fish, the digitization stage is
included inside the fish and digital data are directly transmitted on board. The acquisition
system simply stores the data coming through the digital interface (USB or Ethernet Link).
For analog fish, the digitization stage is executed by the acquisition software on the PC
board. The A/D board is plugged into the PC. In this case, the following main acquisition
parameters need to be selected:
• Gain adjustment: If the sonar fish delivers an analog signal, gain adjustment may be
needed. DELPH Sonar Acquisition uses a 24 or 16 bits A/D converter, eliminating the
need to apply any gain before the A/D stage.
• Number of Channels cN : Either 2 or 4 channels for dual-frequency side-scan.
• Sonar Range R
• Sampling Frequency sf : In order to meet the Nyquist criteria, the sampling frequency
should be at least twice the bandwidth of the acoustic signal. In DELPH the sampling
frequency is 24 KHz by default.
• Digitization: The number of bits per sample bbN . This is commonly 12 or 16 and now
24 bits/samples A/D.
• Data Flow Rate.
On the basis of the above, one important parameter can be deduced: the data flow rate
sφ is defined as the number of samples recorded per second:
scs fN *=φ
In terms of number of bits / second this then gives:
bbscb NfN **=φ
For example, for a dual-frequency sonar digitized at 24kHz using a 24 bits A/D converter,
this gives a data flow rate of 144 kb/s or 518 Mb/h.

I.6 Sonar Positioning
Alongside sonar data acquisition, the system also records all the necessary position in-
formation data, in order to be able to compute the exact position of any point in the image.
The position of a given sample in the scan is computed in two steps:
• Computation of the position of the acoustic center of the sonar fish
• Computation of the position for every sample in the scan
The geometry of the acquisition should have been defined. There are two main configura-
tions:
• The fish may be hull-mounted on a positioned system (boat, ROV, etc.)
• The fish may be towed
In each case, fish position and heading are computed using information on the mounting
offset between each item of equipment. (GPS, winch, pinger, etc.). Figure 21 shows the
offset computation for a towed fish:
( )22
ZHLdX +−+=
Figure 21 – Computing the Position of a Towed Fish
Sample position is obtained by (see Figure 22):
• Interpolation of fish position at time T = (Temission + Treception) / 2
• Computation of the ground range R
• Computation of the true geographical position using the fish heading
First Step
Second Step

Figure 22 – Computing a Sample Position
At short range, it is usually assumed that the fish has not moved in the interval between
ping emission and ping reception.
The roll angle has no effect on positioning but the amplitude of the sonar return is affected
since the beam pattern will have rotated. The pitch angle induces a small effect by shifting
the line along the track forward or backward from the vertical. The pitch effect is usually
negligible in terms of along-track resolution (a few tenths of a dm) for an altitude in tens of
meters.
Attitude Mo-
tion Effect

I.7 Sonar Data Processing and Interpretation
I.7.1 INTRODUCTION
The two fundamental goals in side-scan processing are target detection and seafloor
classification. Where detection is concerned, this requires precise computation of the posi-
tion of the target and good radiometric correction and noise filtering applied to the signal in
order to enhance target image contrast. For classification purposes, the radiometric cor-
rection should enable retrieval of true bottom reflection strength. Figure 23 contains a flow
chart for the processing of side-scan imagery data. There are two main processing
groups.
• A low-level set of functions to build the best possible side-scan mosaic image
• High-level processing such as target detection and seafloor classification
In this document we focus on the low-level functions.
Figure 23 – Side-scan Image Processing

I.7.2 LOW LEVEL PROCESSING
As described in Figure 23, first, fish altitude needs to be known. This parameter is re-
quired for later processing steps such as radiometric correction and sample position com-
putation. If the sonar fish is not equipped with an altimeter, this parameter is estimated
from the sonar signal itself. This is described in section I.7.3.
The following processing step is to enhance the sonar signal: even if the sonar fish in-
cludes a gain adjustment function it is always better to reprocess the raw signals, choos-
ing radiometric processing functions specifically to suit different purposes (detec-
tion/classification). This is explained in section I.7.4. Some aspects of sonar image inter-
pretation such as Annotations, Echo Analysis or Measurement can be done on a line-by-
line basis with the sonar data displayed in a waterfall window, but the final stage involves
constructing a fully geo-referenced mosaic image by merging individual survey lines. This
makes it possible to export the sonar image and interpretation to GIS software for further
merging and analysis of data.
I.7.3 SEAFLOOR DETECTION
It is assumed that the time of arrival of the first significant echo in the sonar signal will give
a value for fish altitude.
In fact the first significant echo is the closest and brightest echo in the slant range direc-
tion (see Figure 24). This assumption is valid if a relatively flat sea bed is assumed and if
the beam pattern in the vertical direction is broad enough for a specular reflection from the
fish nadir to be observed. Numerous types of algorithm have been developed for seafloor
tracking. They usually give good results when the seafloor has a satisfactory index (such
as sand or gravel) but detection performance never attains 100%. The upshot is that
semi-automatic methods allowing manual deletion or editing of parts of the detection re-
sults are always used in practice at the final stage of the detection in order to arrive at a
perfect result.
Figure 24 – Altitude Measurement from a Side-Scan Signal: Limitations

I.7.4 RADIOMETRIC CORRECTION
The acoustic signal level received from a target/bottom is neither the true bottom reflectivi-
ty level nor the target strength: the signal will have been attenuated by propagation and
spreading to a degree dependent on range and it will also have been modulated by the
beam pattern. One of the goals of radiometric correction is to compensate for such range
and beam angle variation in order to estimate bottom reflectivity.
In accordance with the notations contained in Figure 25, the relationship between true ref-
lectivity A(M) at point M(r,θ) and the raw acoustic signal Sr(M) is:
( ) ( ) ( ) ( ) ( ) ( )rLBMAMPMAMSr ** ϕ== with ( )rθψθ
π
ϕ +−+=
2
• ϕ is the beam pattern angle of the current point M,
• ψ is the beam pattern tilt angle and θr is the roll angle,
• P(M) is the global attenuation function which can be expressed as the product of the
two functions L(r), attenuation with range, and B(ϕ), the beam pattern function.
These two functions can be estimated using the following calibration procedure:
On a selected flat and homogeneous seabed (assuming A(M) = A), the sonar signal is
recorded at different heights. The calibration functions Bref(ϕ) and Lref(r) are computed as
the mean signal level around each (ϕ, r) value.
The corrected signal Sc(M) is then obtained as:
( ) ( )
( ) ( )MLMS
MS
SMS
refref
r
c 0=
where S0 is a nominal average level.
However, in practice, this procedure can be simplified by varying only one variable: either
the range r or the beam angle θ. This assumption is clearly valid for a flat or nearly flat
bottom since in that case range and beam angle are linked by the following relation: Z(M)
= r tan(θ).
The advantages of beam angle compared with range correction are:
• better compensation near nadir, where the beam angle varies rapidly,
• correction of roll angle variation.
This procedure can also be done systematically (i.e. the calibration curve is updated on-
line) to obtain an automatic gain control function (range or beam angle). In that case the
function equates more to a normalization of the signal than to true compensation: the
mean average level of the corrected signal is kept constant (either in range or in angle)
hence suppressing any information on the true reflectivity of the seafloor. The result of this
correction on a set of sonar data is illustrated in Figure 26.

Figure 25 – Radiometric Correction: Notations
Figure 26 – Side-Scan Image Before and After Radiometric Normalization

I.7.5 SONAR IMAGE GEOMETRIC CORRECTION: IMAGE MOSAICKING
After radiometric correction, the sonar signal needs to be corrected for geometric distor-
tion to retrieve the right dimension/orientation and position of image features.
I.7.5.1 Slant Range Correction
The first correction is to project the temporal signal on to the ground, converting range tra-
vel time t to across-track coordinate x . This operation is commonly called “slant range
correction”, as described in Figure 27: the across-track distance x is sampled at a sam-
pling interval x∆ so that xi ix ∆= . The sampling interval is chosen according to the
across-track resolution of the side-scan system
2
τc
:
2
τc
x ≈∆
For each across-track sample with depth ( )xh , the corresponding travel time ( )xt is
computed as follows:
( ) ( ) 22
xxhxt +=
The amplitude value ( )xA is interpolated between the two nearest time samples ( )1tS
and ( )2tS such that ( ) 21 txtt << . In practice, the computation is done assuming a flat
seabed i.e. ( ) hxh = .
Figure 28 provides an example of a slant corrected image.
Figure 27 – Slant Range Correction Principle

Figure 28 – Slant Correction
I.7.5.2 Image Geo-referencing
In the slant corrected image, the objects are represented with their actual across-track di-
mension. In the along-track direction the ping interval in time should be converted to a
ping interval in meters according to current boat speed in order to ensure that the shapes
of objects are correctly represented. This correction is called speed correction. In the final
step, the image should be projected according to the local boat heading to retrieve the
correct image orientation. These operations involving projection onto a geographical grid
are commonly called image mosaicking or image geo-referencing. The mosaicking
process comprises a number of processing steps such as 2D filtering, down-sampling and
bilinear interpolation. On completion of the image mosaicking process the waterfall image
is transformed into a raster image with constant resolution or pixel size. Pixel size ∆ or
mosaic resolution should be selected to ensure that it is greater than the minimum spatial
resolution provided by the side-scan sonar. Minimum spatial resolution is usually the
across-track resolution
2
τc
so that
2
τc
>∆ . An example of the transform is shown in
Figure 29.
Figure 29 – An Example of Image Geo-referencing

I.7.6 OBJECT MEASUREMENT (WIDTH/LENGTH/HEIGHT, POSITION)
Using the side-scan image of an object, it is possible to estimate a simple geometric mea-
surement such as length, width and height. As illustrated in Figure 30, the height is esti-
mated by measuring at least two points in the scan line: the beginning and end of the
shadow. If bt and et are the time values of these points, the object height estimated using
shadow length will be
( )
e
be
t
tt −
=
D
H , where D is the object depth below the sonar fish.
The estimation can be improved by taking into account the beginning of the echo (t0). This
enables the minimum and maximum heights of the object to be computed. The minimum
height is obtained using the full length, the echo and shadow length
( )
o
oe
t
tt −
=
D
Hmax ,
HHmin = .
Figure 30 – Two Different Ways of Computing the Height of an Object

II OPERATING THE SOFTWARE
II.1 Software Architecture
Figure 31 – Software Architecture
The DELPH Sonar software is composed of two main components. See Figure 31:
• DELPH Sonar Acquisition software is dedicated to data storage in standard XTF format
(eXtended Triton Format file).
• DELPH Sonar Interpretation software contains numerous modules: interpretation, con-
tact analyzer and mosaic viewer processing XTF raw data files.
The software runs on a standard PC platform using Windows XP. Hardware and software
installation procedures are described in detail in the DELPH Sonar Acquisition and
DELPH Sonar Interpretation User’s Manuals.
The interpretation software can be run in either of two modes: real-time or post-
processing.

II.2 Data Acquisition and Storage
II.2.1 ARCHITECTURE
Figure 32 – Acquisition Software
DELPH Sonar Acquisition records and stores sonar and positioning data output from ex-
ternal devices. See Figure 32. System geometry needs to be specified (mounting offset,
cable layout) in order to ensure correct positioning of the sonar data. Before starting any
acquisition, the following three main sets of acquisition parameters must be carefully con-
figured:
• Sonar acquisition parameters
• Serial/Ethernet port configuration
• System Geometry
In the DELPH Sonar Acquisition User’s Manual, a detailed explanation of how to set these
parameters is provided. However, further details on sonar acquisition are provided in the
following section.

II.2.2 MAIN IMPORTANT FEATURES OF SONAR ACQUISITION
There are two kinds of sonar device: analog side-scans delivering an analog signal output
(usually two signals: one for the port antenna and the second for the starboard antenna)
and digital side-scans which output sonar data in a digital format, generally via an Ether-
net or USB link. Dedicated server software handles communication (acquisition and com-
mand control) between the fish and the DELPH Sonar Acquisition software.
In modern digital side-scan technology, communication goes via an Ethernet cable or
USB link. Command control of the fish is in this case integral to the server. The main dif-
ference between the digital and analog interfaces is that the sampling frequency of the
A/D converter needs to be selected in the analog interface. By default, the sampling fre-
quency is set at 24 kHz but can be increased up to 48 KHz. A sampling frequency greater
than twice the signal bandwidth should be selected.
When using an analog server, it is also possible to select a range smaller than the ping
interval of the sonar. This may be done for example to avoid recording data at far range,
thus saving disk space and processing time. In any case, it is important to record the raw
data from the sonar, disabling any TVG function inside the sonar fish.
Digital
Analog

II.3 Data Processing and Interpretation
Figure 33 – The Interpretation Software
In real-time, DELPH Sonar Interpretation processes the data as it is stored in the XTF
files. In actual fact, the acquisition software runs on one PC and the interpretation soft-
ware can be executed on a second, remote PC.
As shown in Figure 33, the acquisition and interpretation software are connected by the
DELPH Real-Time monitor module. In post-processing, the stored raw data can be repro-
cessed. Figure 34 shows how to run the interpretation software in real-time post-
processing modes.
Figure 34 – Starting the Interpretation Software

All the processing functions are available in real-time or in post-processing modes. Figure
35 contains a processing function flow chart.
First, the sonar altitude needs to be known. If there is no altimeter, fish altitude can be es-
timated as described in part I.7.3 by tracking the first significant return in the sonar signal
for each scan.
Figure 35 – Processing Flow-Chart
Following this, radiometric correction functions either in slant range or in beam angle are
applied to arrive at an enhanced sonar image. The slant correction function and geo-
referencing functions correct the image for geometric distortion. These functions are easily
accessible and configurable in the processing control panel of the user interface shown in
Figure 36. A second panel is dedicated to annotations and area exclusion tools.
Figure 36 – GUI

II.3.1 AUTOMATIC BOTTOM DETECTION
As explained in Part I.7.3, fish altitude is estimated by tracking the first significant echo on
each sonar scan. In the DELPH Sonar Interpretation software, the algorithm computes a
cost function for each sample in a search window. The sample that gives the highest cost
value is selected as the first return.
The search window is limited by user-selected minimum and maximum altitude values (in
actual fact these are slant range values and not altitude values). See the minimum and
maximum selection in Figure 37. By default, the maximum altitude value is set to the mid-
dle of the range. A longer search window increases the processing time proportionally.
The chosen minimum altitude value should be not too high (typically a few meters) in or-
der to avoid clipping detection. This parameter helps to track the seafloor when there is a
high level of noise in the water column at the beginning of the scan.
A low pass filter is then applied to smooth the detection. In DELPH Sonar Interpretation
software, the low-pass filter is simply a moving average. The filtering window length of the
filter is a user-defined parameter.
Detection is applied to the port and starboard channels for each scan and the final result
is the minimum altitude detected on port and starboard. For dual-frequency sonar, bottom
detection is done on the low-frequency channels. Following automatic detection, it is poss-
ible to modify the results using the bottom-editing function.
Figure 37 – Bottom Detection Parameters
Interval
Filter
Detection

II.3.2 RADIOMETRIC CORRECTION
As explained in Part I.7.4 and as shown in Figure 38, the side-scan sonar signal is atte-
nuated at the far range due to signal absorption and spread. The radiometric correction
functions compensate for this effect in order to obtain a signal with good contrast over the
whole scan. Radiometric correction is achieved by multiplying the sonar data with a gain
curve.
It is also necessary to compensate for any electrical offset in the sonar signal by subtract-
ing a constant value from the sonar. This offset correction is applied before gain curve
multiplication. Offset correction increases the dynamic of the signal, which produces an
image with enhanced contrast.
The corrected signal ( )tSc is related to the raw signal ( )tS as follows:
( ) ( ) ( )[ ]OffsettStGtSc −= * where ( )tG is the gain correction curve
In the DELPH Sonar Interpretation software, the user can choose between three types of
algorithm for computing the gain curve, one non-adaptive gain correction and two auto-
matic:
• Time Varying Gain (TVG)
• Automatic Gain Control (AGC)
• Beam Angle Correction (BAC)
Figure 38 – The Side-Scan Image Before and After Radiometric Correction
In the first method, the signal is corrected by applying a user-defined fixed gain curve.
This method is called Time Varying Gain (TVG). It is not adaptive. Each scan of the sonar
line is corrected using the same gain curve. In DELPH Sonar Interpretation, you can de-
fine a gain curve specific for each channel (Port/Starboard, High and Low frequency). In
Gain
TVG

the two other methods the gain curve is computed from the data, with the result that the
gain curve will vary between scans.
When using the TVG method, contrast reflectivity due to seafloor type is preserved (low
reflectivity for mud, high reflectivity for sand).
Contrary to the above, when using an adaptive method, AGC or BAC, the sonar signal is
normalized to produce a constant average across-track value, thus attenuating the reflec-
tivity contrast due to seabed type. Figure 39 provides an illustration of this effect. The
same image has been processed using adaptive and non-adaptive methods. In other
words, the first method is more appropriate for seabed classification purposes and adap-
tive methods are more appropriate for detection. In addition, when mosaicking the sonar
lines, adaptive methods produce more homogeneous mosaic images.
Figure 39 – Comparison between Adaptive (AGC) and Non-adaptive (TVG) Gain Correction
II.3.2.1 Offset Correction Parameter
The only parameter is the offset value in mV. This can be a negative or a positive value.
The default is 0 mV. It is best estimated when playing back the data in the DELPH Sonar
Acquisition software, when the raw signal data can be viewed in the oscilloscope-like win-
dow. The offset roughly corresponds here to the lowest signal level in the water column. If
the offset is set too high, the image will become darker (in direct display mode). In Figure
40, we show the effect of applying a small offset value: image contrast is improved.
AGC and BAC

Figure 40 – Offset Correction: left 35 mV, right 0 mV

II.3.2.2 Time Varying Gain
There are 5 parameters for Time Varying Gain. See Figure 41. Four are used to set the
shape of the curve, and the gain factor gives the overall scale factor:
• Gain value at beginning (t = 0)
• Gain value for the intermediate point
• Range value for the intermediate point
• Gain value at the end of the scan
• Gain factor: overall scale factor
Figure 41 – TVG Parameters
The gain curve is constructed using the 4 parameters as a concatenation of two conti-
nuous parabolas. The gain value is expressed in percentage of the Gain Factor parame-
ter. For instance, if the gain factor is set to 100 and the final gain is set at 80%, the sonar
signal value will be multiplied by 100 x 80 / 100 = 80 at the end of the swath.
A typical gain curve is shown in Figure 42.
Figure 42 – A Typical Gain Curve

II.3.3 AGC CORRECTION
The AGC correction function is a normalization of the signal by time (or slant range) ac-
cording to a reference level refA . The algorithm begins by computing for each item of raw
ping data ( )tSi
an average signal ( )>< tSi
that is computed on a small window around
each data sample. The gain correction ( )tGi
is then obtained through the inverse of this
average signal multiplied by the reference level:
><
=
)(
)(
tS
A
tG i
refi
Next, the gain correction curve is low-pass filtered by an exponential filter with strength α
. And finally, the filtered gain curve ( )tG
i
f at ping index i has the form
)()()1()( 1
tGtGtG i
f
ii
f
−
×+×−= αα
and the corrected signal ( )tSi
c is obtained as follows:
)()()( tStGtS ii
f
i
c ×=
This is illustrated in Figure 43 below.
Figure 43 – Sonar Normalization in Time (or slant range)
In the user interface, see Figure 44, the two parameters to be set are:
• The Average Level, this being the reference level in percentage of the full-scale value
of the signal. The full-scale value is the output dynamic of the A/D converter in Volts.
Typical values for the average level are in the range 35-50%. Increasing the average
level then amplifies the signal. If an excessively high value is selected the strongest
echoes will be clipped at the maximum value.

• The Filtering Window length in meters gives the strength of the exponential filter. A
small filtering window value corresponds to a high degree of normalization. Converse-
ly, setting a larger filtering window decreases the degree of normalization. As a rule of
thumb, the filtering window length must be greater than the maximum size of image
features. Typical values are around 10 - 100m.
Figure 44 – AGC Parameters
II.3.4 BAC CORRECTION
As explained in part I.7.4, angle normalization is a better choice when the fish is not flying
at a constant altitude above the seafloor. The gain correction curve will then be obtained
from the average of the raw signal for each angle. For each sample at a slant range R, the
angle is computed knowing the fish altitude H as:
R
H
=)cos(φ
which is defined only for sample that has a slant range R greater than fish altitude H.
For a sample with a slant range less than fish altitude the gain value is set to 1. See the
Figure 45 for notations and an illustration.

Figure 45 – Sonar Normalization using Beam Angle
In the user interface, see Figure 46, the BAC parameters are defined as:
• Average level: same meaning as for AGC
• Filtering Windows: same meaning as for AGC
• Bottom type: this parameter is used to determine the fish altitude value. By default the
fish altitude value is the value determined by the tracking algorithm. It is however poss-
ible to set a constant value. This can be useful in an area where bottom detection is
less effective.
Figure 46 – BAC Correction Parameters

II.4 Image Mosaicking
A mosaic image can be constructed from one of multiple survey lines following the steps
described below, see Figure 47:
• Select the Geodesy
• Select the Mosaic File
• Process each file as for radiometric and geometric correction
• Select the processing parameter for mosaic construction
• Build the mosaic image
• View the results in a viewer
The same procedure applies in real-time. The mosaic processing parameters are shown
in Figure 48. There are three key parameters:
• Mosaic resolution
• Heading type
• Merge method
A mosaic is a raster image that is a regular grid always oriented to true geographical
north. Each grid cell has the same size in the north and east directions.
The resolution should be greater than the across-track resolution of the side-scan be-
cause it is the smallest physical resolution achievable by the system. If a larger resolution
cell is chosen, the image will be low-pass filtered before gridding to avoid any aliasing
problem.
The choice of heading type is valid if there is an additional sensor such as a compass
heading in the sonar fish. By default, the heading is computed as the course over ground
(COG) on the filtered positioning data.
When processing multiple survey lines that overlap, the pixel fusion method must be de-
fined. By default, the latest geo-referenced pixel value is kept in the image. The second
option available in DELPH Sonar Interpretation is to select a weighted average value that
computes an average of all the overlapped pixel values. In practice, it is best to mosaic
each survey line independently. It will then be possible to merge all the individual mosaics
using one of the options.
Post-
processing
Real-Time
Resolution
Heading
Fusion

Figure 47 – Procedure for the Construction of a Mosaic Image
Figure 48 – Mosaicking Parameters

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