Optimization of the Geometry of Communication for Autonomous Missions of Underwater Vehicles

UNIVERSITÀ DI PISA 1 34 3 IN

SU

PREMÆ DIGN ITISTA

Universit` a degli Studi di Pisa

DIPARTIMENTO DI INGEGNERIA DELL’INFORMAZIONE Corso di Laurea Magistrale in Ingegneria Robotica e dell’Automazione

Tesi di Laurea Magistrale

Optimization of the Geometry of Communication for Autonomous Missions of Underwater Vehicles

Candidato:

Alessio Micheloni

Matricola 443219

Relatore:

Prof. Andrea Caiti

Controrelatore:

Prof. Lorenzo Pollini

Sommario

Il potenziale dei Veicoli Subacquei Autonomi (AUVs) nel campionamento, mon- itoraggio e sorveglianza dell’ambiente marino è consolidato già da tempo. Uno degli ostacoli più rilevanti nella loro implementazione risiede nelle limitazioni del canale acustico per la comunicazione tra i veicoli. I modelli per la simulazione acustica subacquea hanno un ruolo importante nella predizione di possibili perdite ed errori di trasmissione, in quanto la propagazione del suono sott’acqua può essere misurata o stimata con grande precisione. In questa tesi, dati reali della velocità del suono provenienti da un esperimento (CommsNet 13) sono stati utilizzati nella simulazione delle condizioni ambientali allo scopo di analizzare la comunicazione acustica tra un veicolo USBL sulla superficie del mare e un modem acustico po- sizionato sul fondale.

Abstract

The potential of Autonomous Underwater Vehicles (AUVs) working as a team in

sampling, monitoring and surveillance of the marine environment has been real-

ized since quite a long time. One of the most relevant obstacle to their operational

implementation resides in the limitations of the acoustic channel for inter-vehicle

communications. Underwater acoustic modeling and simulation plays an impor-

tant role in predicting possible losses and transmission failures between them, and

underwater sound propagation can be precisely measured or estimated. In this

thesis, sound speed data from a real experiment (CommsNet13) were used to sim-

ulate environmental conditions and analyze acoustic communication between an

USBL-vehicle on the sea surface and an acoustic modem on the sea bottom.

1 Introduction

The development of autonomous underwater vehicles (AUVs) began in earnest in the 1970s. AUVs are now being used for a variety of tasks, including oceanographic surveys, demining, and bathymetric data collection in marine and riverine environ- ments. Accurate localization and navigation is essential to ensure the accuracy of the gathered data for these applications [1–3].

The problem of navigation and self-localization is particularly challenging in an underwater context due to the tight environmental constraints, such as the absence of an absolute positioning system, i.e. GPS, and the small communication bandwidth of the acoustic channel. Underwater communication is difficult due to factors like multipath propagation, time variations of the channel, small available bandwidth and strong signal attenuation, especially over long ranges. One of the most important parameter in this context is the sound speed, since it is the main influence on acoustic ray paths (together with top and bottom reflections).

In this thesis, real sound speed data recorded during the experiment Comm- sNet13 were used in a numerical simulator (BELLHOP) in order to characterize the underwater environment and to predict how wave energy propagates in the channel. This served as the basis for the consequent analysis of acoustic commu- nication between a vehicle on the surface and an acoustic modem fixed on the sea bottom, which can encounter possible losses and transmission failures.

2 Background

In acoustic navigation techniques, localization is achieved by measuring ranges from the time of flight (TOF) of acoustic signals. Common methods include the following [1]:

• Ultrashort Baseline (USBL)

• Short Baseline (SBL)

• Long baseline (LBL)

Each of these localization procedures has its own advantages and disadvantages.

In the framework of the THESAURUS project (Italian acronym for "TecnicHe per l’Esplorazione Sottomarina Archeologica mediante l’Utilizzo di Robot aU- tonomi in Sciami") a class of AUVs (called Typhoon) able to cooperate in swarms to perform navigation, exploration and surveillance of underwater archaeological sites has been developed.

During the CommsNet13 experiment [4–9], which took place in September 2013

in the La Spezia Gulf, North Tyrrhenian Sea, one of these vehicles was used to

test a mixed USBL/LBL system for navigating an AUV within a pre-specified

area. In particular, the AUV was equipped with a USBL sensor, capable also

to communicate as an acoustic modem; the LBL anchors were acoustic modems

deployed at the seabed in a-priori unknown locations (i.e. without performing the

standard position calibration of LBL nodes). The AUV was also equipped with an

0 50 100 150 200 0

20 40 60 80 100 120 140 160 180

East direction [m]

North direction [m]

L2

J1

T1 1 2

3

4

5

6

GPS OFF

Typhoon programmed route GPS

EKF (GPS on) EKF (GPS off) USBL fixes

(a)

0 100 200 300 400 500 600

0 5 10 15 20 25

Time [s]

Error [m]

Error USBL fixes Final Error

(b)

Figure 1: Estimated path and navigation error for Sept. 22th run (adapted from [4]).

Inertial Measurement Unit (IMU), and with a Global Positioning System (GPS) receiver antenna.

Conceptually, the proposed localization procedure is as follows:

• Initialization Phase: the AUV, navigating at the sea surface (knowledge of absolute position in a standard NED frame is available thanks to GPS receiver), interrogates repeatedly the moored modems, not necessarily from the same position. The measured relative positions are transformed into absolute positions by exploiting the GPS information on the absolute position of the USBL modem (i.e. the one mounted on the vehicle). The set of measured absolute positions from each moored modem are averaged, in order to obtain a final estimate of each moored modem absolute position.

• Navigation Phase: the AUV navigates with the GPS turned off, pinging the moored modems and measuring the relative position of the moored modems with respect to the on-board USBL modem; taking into account the knowl- edge of the moored modem absolute position, the USBL modem relative position is transformed into an absolute position. This absolute position es- timate is fed to the navigation filter, fusing together IMU measurement and acoustic position estimates.

The estimated path for Sept. 22th run of the experiment is reported in figure 1(a), together with the GPS path, which is taken as ground truth comparison. The red diamonds in the figure indicates the acoustic position measurements, while J1, L2 and T1 represents the moored modems positions (obtained after the initialization phase). In figure 1(b) we also report the estimation error, computed as the distance (norm) in meters between the filter estimated position and the GPS position as a function of time. The green diamonds represent the instant in time in which an acoustic position measurement is received by the filter.

From both figures it can be noted the expected drift in position estimate in

the absence of acoustic measurements data (when navigation relies on IMU dead

reckoning only), and how the acoustic position fixes are effective in decreasing the

0 50 100 150 200 0

20 40 60 80 100 120 140 160 180

East direction [m]

North direction [m]

L2

J1

T1 1 2 3 4

6 7 5

8

9

10

11

Typhoon programmed route EKF

Delivered Pings Failed Pings USBL fixes

Figure 2: Acoustic performance of the moored modems network (adapted from [4]).

1515 1520 1525 1530 1535

0 5 10 15 20 25

30

Sound Speed (m/s)

Depth (m)

(a) Morning

1515 1520 1525 1530 1535

0 5 10 15 20 25

30

Sound Speed (m/s)

Depth (m)

(b) Afternoon

Figure 3: Sound speed profiles recorded during CommsNet13 sea trials on Sept.

14th run.

navigation error. As an additional consideration, it is notable how the interval in time between two successive measurements can indeed be of minute scale.

Figure 2 shows the spatial distribution of the modem interrogations: in partic- ular, the green circles and the red crosses indicate the position of the vehicle at the time instant of an acoustic ping, respectively successful or not. A new USBL measurement, indicated by the red diamond, occurs as a result of a delivered ping;

however, it is evident that not all the successful interrogations provide a positioning information. As can be expected, the acoustic communication is strongly depen- dent on the vehicle position at the moment of the interrogation. also the depth of the USBL head has a non negligible influence on the acoustic communication performance.

Figure 3 depicts the underwater sound speed data recorded using a CTD sensor

in the Gulf of La Spezia on the Sept. 14th experimental run, both for morning

and afternoon periods of the day. Sound speed is the most important parameter in

ocean acoustics, and CTD sensors are oceanographic instruments used to determine

the conductivity, temperature and depth of the ocean. As we can see, morning and

afternoon profiles are very similar. They both possess a strong negative gradient

0 100 200 300 400 500 0

5 10 15 20 25

30

Range (m)

Depth (m)

(a) Morning

0 100 200 300 400 500

0 5 10 15 20 25

30

Range (m)

Depth (m)

(b) Afternoon

Figure 4: Ray plots for CommsNet13 environmental conditions.

near the sea bottom, and the only difference is in the upper part (constant for morning, linear for afternoon), that is very sensitive to atmospheric and climatic factors.

These data were chosen as representatives of the acoustic environmental condi- tions during CommsNet13 trials. As previously stated, one of the possible causes of the irregularly time-spaced acoustic fixes relies in the communication losses be- tween the moored modems and the AUV due to acoustic channel effects. Since sound speed completely characterizes the channel properties (together with water and sea bottom geoacoustic parameters), the data pictured in figure 3 can be used to analyze the transmission quality between the moored modems and the moving USBL-vehicle, to better optimize the geometry of communication and therefore to possibly increase the reliability of acoustic fixes. This was precisely the main focus of the thesis.

3 Acoustic Channel Simulations

Sound waves are variations of pressure in a solid or fluid medium. The ocean is an acoustic waveguide limited above by the sea surface and below by the seafloor [10].

Sound speed underwater is influenced by a lot of different factors (temperature, salinity, density, depth,. . . ), and its effect on acoustic ray paths can be resumed with the Snell’s Law [11]:

cos θ

c = const, (1)

which relates the ray angle (w.r.t. the horizontal) θ to the local sound speed c. It is quite straightforward to see that the implication of this law is that sound bends locally towards regions of low sound speed (in other words, sound is "trapped"

in regions of low sound speed): if c increases, θ must decrease to compensate the variation, and vice versa.

This is confirmed by figure 4. These figures represent the acoustic ray paths

in the environmental conditions of CommsNet13 experiments, and were obtained

using a numerical simulator called BELLHOP [12], which falls in the particular

category of ray tracing programs [11]. The simulations were made at the frequency

(a) Morning (b) Afternoon

Figure 5: Transmission loss fields for CommsNet13 environmental conditions.

0 100 200 300 400 500

25 30 35 40 45 50

55

Range (m)

TL (dB)

Isospeed Real Profile

(a) Morning – Depth 0.5 m

0 100 200 300 400 500

25 30 35 40 45 50

55

Range (m)

TL (dB)

Isospeed Real Profile

(b) Afternoon – Depth 5.5 m

Figure 6: Transmission loss slices for fixed receiver depth and comparison with an ideal isospeed SSP.

of 30 kHz, which is the frequency the USBL-modem of the Typhoon was operating at, with the source (red circle) depth equal to 29 m.

If we see figure 3, the region where sound speed reaches its minimum is near the sea bottom. Therefore, influenced by Snell’s Law, all the rays are attracted to the region where sound speed reaches its minimum value, and change their path to get towards it; this creates the convergence zones of the rays near the sea bottom visible in figure 4.

Figure 5 depicts the Transmission Loss (TL) plotted versus depth and range of the waveguide; transmission loss is the standard measure in dB (decibels) of the wave attenuation due to various mechanisms (bottom and top reflection losses, geometrical spreading, losses due to the medium,. . . ). We see that this quantity clearly follows the ray paths: where these are more cluttered, the attenuation becomes lower.

During CommsNet13 sea trials, the Typhoon vehicle (our receiver) was navigat-

ing at low depths, 0.5 m in the morning and 5.5 m in the afternoon, very close to

the sea surface. Figure 6 plots the transmission loss vs. range (horizontal distance

from the source) when the vehicle’s depth is fixed. We made a comparison with

a standard isospeed SSP (sound speed profile), that is, sound speed constant with

depth, in order to appreciate the influence on sound propagation. These figures

S

1 R

2

3

Figure 7: Example of a source-receiver configuration, where three rays (one direct, two reflected) arrive at the receiver.

0.12 0 0.14 0.16 0.18 0.2 0.22

0.5 1 1.5 2 2.5 3 3.5 x 10

Time (s)

Amplitude

(a) Morning

0.12 0 0.14 0.16 0.18 0.2 0.22 0.24

0.5 1 1.5 2 2.5 3 3.5

4 x 10

Time (s)

Amplitude

(b) Afternoon

Figure 8: Arrivals for receiver range 200 m, morning and afternoon conditions.

show that, for our conditions, there is a higher TL (i.e. higher attenuation) of almost 2-3 dB, which is due to the convergence of ray paths previously introduced;

in order to keep the energy balance, ray paths are more dispersed at low depths.

Since we are interested in analyzing the transmission of acoustic signals between the source (moored modems) and the receiver (USBL vehicle), we need to find the impulse response of the linear system that establishes the bond between them. For acoustic channels, the impulse response follows immediately thanks to the so called arrivals, which are the amplitude-delay pairs of the rays which passes, during sound propagation, through a specific (r, z) point of the waveguide. Figure 7 illustrates a simple example where three rays depart from the source and reach the receiver, one direct and two reflected; each of these rays constitute a (possibly attenuated) replica of the original signal coming from the source.

Therefore, if the source sends a signal, the receiver gets its convolution of with

the impulse response of the acoustic channel. Figure 8 show the arrivals sequences

as stem plots for morning and afternoon environmental conditions, when the range

of the receiver is equal to 200 m. The high number of signal replicas, the most of

them spaced at low time intervals, suggest a high interference due to the acoustic

−0.01 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−1.5

−1

−0.5 0 0.5 1 1.5

Time [s]

Amplitude

0.0162 0.0162 0.0162

−1 0 1

Figure 9: Source signal (sweeping sinusoid), with an enlargement.

channel effects.

In order to see what happens at the signal during its propagation, one can calculate the cross-correlation between the source (u(t)) and the received (y(t)) signals:

R _yu (τ ) = X

t

y(t)u(t + τ ). (2)

This quantity is an index of the similarities between them, and has a central role in the next section.

4 Transmission Quality Analysis

The waveform used as the source signal in the transmission quality analysis was a chirp (sweeping sinusoid), which is the standard waveform implemented on the EvoLogics acoustic modem mounted on the USBL-vehicle. The lowest frequency was 18 kHz, while the highest was 34 kHz. The chirp duration was 40 ms, while the total time window of observation was 80 ms. The source signal is represented in figure 9: it is well-known that chirps properties produce an auto-correlation equal to a sinc function with the peak perfectly centered in the origin of the time axis.

If this signal reaches the receiver without interference, we would expect a cross- correlation between them which has exactly a delayed peak (one signal replica).

As for the received signal visualization, the time scale was chosen as the reduced time, which represents a relative time that takes into account the distance between source and receiver.

During the experiment, the vehicle was moving with a horizontal distance from the receiver ranging from 200 to 400 m. Hereinafter, all the results will be shown for receiver range equal to 200 m, since results for other ranges (300 m, 400 m) were very similar.

Figure 10 displays the received signals calculated for morning and afternoon environmental conditions, while figure 11 displays the cross-correlation functions between them and the source signal. It becomes immediately clear that the original signal reaches the receiver heavily distorted and attenuated due to the interference and multipath reflections of the acoustic channel. Cross-correlation shapes clearly reflect the signal replicas depicted in figure 8.

It is impossible for the receiver to correctly detect the signal, since undesired

replicas are very near and attenuated; this means that, even if we set an appropriate

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−0.015

−0.01

−0.005 0 0.005 0.01 0.015

Received Signal

Reduced Time [s]

Amplitude

(a) Morning

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−0.015

−0.01

−0.005 0 0.005 0.01 0.015

Received Signal

Reduced Time [s]

Amplitude

(b) Afternoon

Figure 10: Received signals for morning and afternoon conditions, receiver range 200 m.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−6

−4

−2 0 2 4 6

Input−Output Cross−Correlation

Time [s]

(a) Morning

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−5 0 5

Input−Output Cross−Correlation

Time [s]

(b) Afternoon

Figure 11: Cross-correlation functions for morning and afternoon conditions, re- ceiver range 200 m.

threshold on the cross-correlation for signal detection, the receiver becomes too sensible to fluctuations of the signal (noise, outliers,. . . ).

In order to improve signal detection, if the receiver knows the structure of the basic waveform the source uses to send signals with, it can try to estimate the source signal using a two-step procedure:

• Impulse response estimation (using received signal and basic waveform infor- mations);

• Deconvolution of the original signal (from received signal and impulse re- sponse estimate).

The receiver can then correlate this estimate with the basic waveform in order to check if reception was achieved.

Mathematically, impulse response estimation was done using correlation anal-

ysis [13]; if we suppose a linear relationship with finite impulse response between

0 0.01 0.02 0.03 0.04 0.05

−0.8

−0.6

−0.4

−0.2 0 0.2 0.4 0.6 0.8

Estimated Source Signal

Time [s]

(a) Estimated Signal

−0.08 −0.06 −0.04 −0.02 0 0.02 0.04 0.06 0.08

−400

−200 0 200 400 600

True−Estimated Cross−Correlation

Delay τ [s]

(b) Cross-correlation

Figure 12: Estimated source signal and cross-correlation function for morning con- ditions, receiver range 200 m, when λ = 8.85 · 10 ⁻⁶ .

input and output signals:

y(t) =

M

X

k=0

h(k)u(t − k), (3)

and we apply the correlation operator (2) on both members, we obtain the corre- lation equation:

R ˆ yu (τ ) =

M

X

k=0

ˆh(k) ˆ R uu (τ − k). (4)

Since we know input auto-correlation and input-output cross-correlation estimates, this is a linear system of equations which can be solved in order to find the impulse response estimate ˆ h(k).

The second step, deconvolution, was done using a regularized least squares es- timation procedure. The source signal estimate was calculated with:

ˆ

u = H ^T H + λI
−1

H ^T y, (5)

where H is the deconvolution matrix of the linear system (3) that now is available thanks to the impulse response estimate. The introduction of λ (regularization parameter ) was necessary since matrix H was always near to singularity, for every situation.

Figure 12 shows the result of this estimation procedure for one particular case.

We now see that the cross-correlation between the true and the estimated source signal has a single, very high peak at the axis origin, which is exactly the opposite of what happened in figure 11. This now allows the receiver, after setting the appropriate threshold, to detect the source signal correctly and in a more robust way, since there are no other peaks in the cross-correlation function that could possibly interfere.

The result of the estimate depends on the value of λ, therefore we performed

a sensitivity analysis based on this parameter. We calculated the source signal

estimate for every value of λ on an equally logarithmic-spaced grid between 10 ⁻¹²

−12 0 −10 −8 −6 −4 −2 0 0.2

0.4 0.6 0.8 1 1.2 1.4

Sensitivity to Regularization

Log

λ

Cross−Correlation Penalty

Figure 13: Sensitivity analysis to regularization parameter for morning conditions, receiver range 200 m.

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−0.015

−0.01

−0.005 0 0.005 0.01 0.015

Received Signal (with and without Doppler effects)

Reduced Time [s]

Amplitude

No Doppler Dopplerized

(a) Speed 1 m/s

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

−0.015

−0.01

−0.005 0 0.005 0.01 0.015

Received Signal (with and without Doppler effects)

Reduced Time [s]

Amplitude

No Doppler Dopplerized

(b) Speed 10 m/s

Figure 14: Received signals comparison with and without Doppler effects at range 200 m.

and 1. Figure 13 plots the so called cross-correlation penalty against the log ₁₀ values of the regularization parameter. The cross-correlation penalty p _corr was calculated in this way:

p _corr = P X

i

p ^th _i

, (6)

where P is the cross-correlation peak (between the estimated and the true signals) and p ^th _i are all the other values of cross-correlation function that exceed an a predefined percentage of the peak value. For every case, this percentage was chosen as 5%, which is very precautionary.

The "optimal" value of λ for deconvolution was chosen as the one which max- imizes the penalty p corr . This was done in order to obtain the estimate which has the most similar cross-correlation function to that of a chirp signal (peak at the beginning, very small values elsewhere). For every possible source-receiver configuration, we could find this optimal value.

The results showed in the previous sections are valid for the case of stationary

source-receiver configuration. Since the receiver (the USBL-vehicle) was moving

during the experiment, the frequency shifts due to Doppler effects [14, 15] must be taken into account during received signal calculations.

Figure 14 shows the comparison between the received signals calculated with and without Doppler effects, when the horizontal speed of the vehicle is equal to 1 m/s (2 knots) and 10 m/s (20 knots); the second case is out of the range of interest during the experiment. The only substantial difference from the stationary case is a slightly higher attenuation of the signal, while its shape remains the same. At much higher speeds, figure 14(b), there is also an appreciable time shift, but once again the signal shape remains the same.

We used the dopplerized signals in the reconstruction procedure previously described, and results didn’t change. This means that the Doppler shifts do not influence the impulse response of the acoustic channel, and become significant only at very high relative speeds between source and receiver.

5 Conclusions

Starting with real data of sound speed profiles, and using the ray tracing numeri- cal simulator BELLHOP with the appropriate settings, the acoustic environmental conditions of the La Spezia Gulf during CommsNet13 trials were completely char- acterized. We saw that the strong negative gradient of measured SSPs near the bottom produces a convergence of the ray paths, which translates in a slightly higher TL at low depths.

Then, an extensive analysis was done of the transmission quality between a source (the USBL-vehicle) and a receiver (the moored modems) in these condi- tions. Using a standard chirp waveform as the source signal, we realized that the interference and multipath effects produced by the acoustic channel greatly worsen the signal transmission. Assuming that the receiver knows the basic waveform the source uses to communicate with, it could follow a mathematical procedure to re- construct the source signal. The results obtained with this procedure showed that the receiver can get rid of all the interference and greatly improve the transmitted signal reception. Both the cases of stationary and non-stationary source-receiver configuration were analyzed.

The contents of this thesis were elaborated in order to try to explain the ir- regular acoustic position fixes encountered in the localization of the USBL-vehicle during CommsNet13 sea trials. But since we showed that the acoustic channel influences can be eliminated with an appropriate reception algorithm, they could only be explained in two ways:

• The acoustic modems were working at the limit of their detection threshold, which means that the little changes in TL between isospeed and measured profiles (figure 6) become significant.

• Fluctuations in sound speed values and/or other physical parameters can have some influence and therefore they must be taken into account.

More data must be collected and further analyses and simulations must be done

in order to more accurately explain these phenomena.

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