**Optimization of the Geometry of **

**Optimization of the Geometry of**

**Communication for Autonomous **

**Communication for Autonomous**

**Missions of Underwater Vehicles **

**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

**Waves Paths** 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

**Source Signal** 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

Now we write the linear system in this form:**𝒚𝒚 = 𝐻𝐻𝒖𝒖 **

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

𝒖𝒖� = (𝐻𝐻𝑇𝑇**𝐻𝐻 + 𝜆𝜆𝜆𝜆) **−1𝐻𝐻𝑇𝑇**𝒚𝒚 **

* 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