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

Optimization of the Geometry of

Communication for Autonomous

Missions of Underwater Vehicles

Candidato Alessio Micheloni 443219 Relatore Prof. Andrea Caiti

Controrelatore

Prof. Lorenzo Pollini

Anno Accademico 2014/2015

Outline

 Introduction and Background

 Acoustic Channel Simulations

 Methods and Parameters  Results and Discussion

 Transmission Quality Analysis

 Cross-Correlation Approach  Source Signal Estimation

 Source-Receiver Motion

Introduction

 Autonomous Underwater Vehicles (AUVs)

 AUVs navigation and localization

 Acoustic-based sensors and communication

 Underwater Acoustics

 Influenced by speed of sound

 Numerical models for sound propagation

 Goals of the thesis

 Characterization of a real acoustic channel  Analysis of signals transmission

Underwater Acoustic Localization

 Acoustic navigation techniques

 AUV localization is achieved by measuring the

time of flight (TOF) of acoustic signals

The CommsNet13 Sea Trials

 Framework: the THESAURUS project

 Project specifications and results

 Development of Typhoon AUV class

 Localization: mixed USBL/LBL approach

 Localization procedure

 USBL-modem equipped on the vehicle

 Acoustic modems deployed at the bottom  Initialization and navigation phases

The CommsNet13 Sea Trials

 Navigation Path

Irregularly spaced position fixes from acoustic

modems, how can we explain this?

The CommsNet13 Sea Trials

 Spatial distribution of modem interrogations

There is a correlation between the AUV position and the unsuccesful modem interrogations.

The CommsNet13 Sea Trials

 Spatial distribution of modem interrogations

Possible explanation: communication losses

The CommsNet13 Sea Trials

Underwater sound speed profiles measured

with a CTD sensor during the experiment.

Sound speed represents the most important

Acoustic Channel Simulations

Available Software

 Ocean Acoustics Library (website)

 Acoustic Toolbox (by Mike Porter) contains the

most common numerical models

 AcTUP (A. Duncan, A. Maggi), is a MATLAB®

GUI-wrapper for Acoustic Toolbox

For our purposes we chose BELLHOP, a ray

Acoustic Channel Simulations

Simulation Parameters

 Frequency: 30 kHz  Bottom Depth: 30 m  Source Depth: 29 m

 Receiver Depth: 0.5 m (morning) and 5.5 m

(afternoon)

Channel Characterization

 Acoustic waves paths

Acoustic Channel Simulations

 Ray formalism

 Waves modeled as complex amplitude-phase pairs

𝑝𝑝 𝑟𝑟, 𝑧𝑧 = 𝐴𝐴 𝑟𝑟, 𝑧𝑧 𝜃𝜃 𝑟𝑟, 𝑧𝑧

 Snell’s Law: ratio cos 𝜃𝜃 / c is always constant

 Arrivals (or eigenrays)

 Impulse response of the acoustic channel

Waves Attenuation

 Transmission Loss (measured in dB)

Results – Ray Paths

Results – Transmission Loss

Results – Arrivals

 Morning, 200 m

Received signal calculation as the convolution with

the impulse response of the channel: 𝑟𝑟 𝑡𝑡 = ℎ 𝑡𝑡 ∗ 𝑠𝑠(𝑡𝑡)

Transmission Quality Analysis

 We now consider a source-receiver pair in a

fixed configuration (no relative motion)

 Transmitted signals encounter interference

Transmission Quality Analysis

 We now consider a source-receiver pair in a

fixed configuration (no relative motion)

 Transmitted signals encounter interference

and multipath effects due to reflections  A simple example

Transmission Quality Analysis

To see what happens at the original signal, the

cross-correlation function can be calculated: 𝑅𝑅_{𝑠𝑠𝑠𝑠}(𝑘𝑘) = � 𝑠𝑠 𝑡𝑡 𝑟𝑟(𝑡𝑡 + 𝑘𝑘)

Transmission Quality Analysis

 Chirp (sweeping sinusoid)  Lowest frequency: 18 kHz  Highest frequency: 34 kHz  Duration: 40 ms

Cross-Correlation Approach

 Conditions: morning, range 200 m

Cross-correlation emphasizes the interference of attenuated signal replicas.

Source Signal Estimation

 Signal detection

 Threshold value on cross-correlation

 We are too sensible to signal fluctuations

 Idea! Source signal estimation

 Implemented on the receiver

 Substantially improves signal detection

 Two-steps estimation procedure

 Impulse response (correlation analysis)  Source signal (deconvolution)

Impulse Response Estimation

We suppose a linear system between input and output signals:

𝑦𝑦(𝑡𝑡) = � ℎ 𝑘𝑘 𝑢𝑢(𝑡𝑡 − 𝑘𝑘)

∞ 𝑘𝑘=0

The correlation equation then becomes:

𝑅𝑅�_{𝑦𝑦𝑦𝑦} 𝑡𝑡 = � ℎ�(𝑘𝑘)𝑅𝑅�_{𝑦𝑦𝑦𝑦}(𝑡𝑡 − 𝑘𝑘)

𝑀𝑀 𝑘𝑘=0

Writing out this equation for 𝑡𝑡 = 0,1, … , 𝑀𝑀 we get

a linear system so we can calculate the impulse

Deconvolution via Least Squares

𝒚𝒚 = 𝐻𝐻𝒖𝒖

To calculate the input signal estimate we perform the following regularized deconvolution:

𝒖𝒖� = (𝐻𝐻𝑇𝑇𝐻𝐻 + 𝜆𝜆𝜆𝜆) −1𝐻𝐻𝑇𝑇𝒚𝒚

The regularization parameter 𝜆𝜆 is needed because

convolution matrix 𝐻𝐻 is close to singularity (diagonal loading).

Deconvolution - Results

Results for morning, 200 m case when 𝜆𝜆 ≈ 10−5.

Signal detection can now be based on the cross-correlation between true and estimated signals.

Sensitivity to Regularization

Sensitivity to Regularization

Optimal value of 𝜆𝜆 selection:

𝑎𝑎𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑝𝑝_{𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠} = _{∑ 𝑝𝑝}𝑃𝑃

𝑖𝑖

 𝑃𝑃 = cross-correlation peak (highest value)

 𝑝𝑝_𝑖𝑖 = values above a specified threshold of 𝑃𝑃

The resulting correlation function becomes the

Sensitivity to Regularization

Optimal value of 𝜆𝜆 selection:

𝑎𝑎𝑟𝑟𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑝𝑝_{𝑐𝑐𝑐𝑐𝑠𝑠𝑠𝑠} = _{∑ 𝑝𝑝}𝑃𝑃

𝑖𝑖

Doppler Shifts

 Actually, the receiver (USBL-vehicle) keeps

moving while communicating with the modem

 Doppler shifts due to relative motion

 Source-receiver relative speed 𝑣𝑣  Proportional to ratio 𝑣𝑣/𝑐𝑐

 BELLHOP includes a specific algorithm

 Vehicle speeds:1-1.5-2 knots

Doppler Shifts

 Speed: 1 m/s

Doppler shifts do not change the envelope of

the signals. Signals estimation is not affected.

Conclusions and Future Work

 What we have done

 Acoustic channel characterization

 Extensive analysis of transmission quality

 We have shown that acoustic channel effects

can be eliminated using a simple reception

algorithm

 Irregular position fixes – possible explanations

 Acoustic modems were working at the limit of

their detection threshold

 Fluctuations of sound speed and/or other physical