• Aucun résultat trouvé

Watermark: bch1 Dataset Description

N/A
N/A
Protected

Academic year: 2021

Partager "Watermark: bch1 Dataset Description"

Copied!
4
0
0

Texte intégral

(1)

HAL Id: hal-01404491

https://hal.archives-ouvertes.fr/hal-01404491

Preprint submitted on 28 Nov 2016

HAL is a multi-disciplinary open access archive for the deposit and dissemination of sci- entific research documents, whether they are pub- lished or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Watermark: bch1 Dataset Description

François-Xavier Socheleau, Antony Pottier, Christophe Laot

To cite this version:

François-Xavier Socheleau, Antony Pottier, Christophe Laot. Watermark: bch1 Dataset Description.

2016. �hal-01404491�

(2)

1

W ATERMARK : BCH 1 Dataset Description

Francois-Xavier Socheleau, Antony Pottier, Christophe Laot

Institut Mines-Telecom; TELECOM Bretagne, UMR CNRS 6285 Lab-STICC Email: {fx.socheleau, antony.pottier,christophe.laot}@telecom-bretagne.eu

I. I NTRODUCTION

This document describes the BCH 1 dataset that can be used as an input of the W ATERMARK simulator developed by FFI [1]. W ATERMARK is a benchmark for testing, development, and comparison of physical-layer schemes for underwater acoustic communications.

The BCH 1 dataset, provided by Telecom Bretagne, contains four time-varying channel impulse responses (TVIR) measured in the commercial harbor of Brest (France) in the frequency band from 32.5 to 37.5 kHz. The TVIRs were obtained simultaneously with a single-input multiple-output (SIMO) configuration. Each TVIR has a run time of 1 minute. The experiment set-up as well as a description of the TVIRs are presented in the next sections. Please make appropriate references to this document if you use any of these data.

II. E XPERIMENT SET - UP

Channels measurements were conducted in the Brest harbor in May 2015 [2]. The transmissions were realized between two docks over a 800 m distance, in a 20 m water depth (see Fig.

1). At the transmitter side, an omnidirectional transducer ITC- 1032 (resonant frequency: 35 kHz) immersed at a 2 m depth was used. At the receiver side, four broadband omnidirectional BK-8106 hydrophones were vertically deployed at a depth between 3 and 6 m with a 1 m spacing. Estimates of the TVIRs were obtained by successive matched filtering to a known probe signal transmitted repeatedly [3]. The probe signal used during the experiments was a m-sequence of 511 BPSK chips transmitted at a symbol rate of 5 kbds. Such a sequence can capture arrivals delayed up to 102 ms and channel estimates can be updated up to 9.78 times per second. Measurements were made at a carrier frequency of 35 kHz and transmitted symbols were pulse-shaped by a root raised cosine filter with a roll-off factor of 0.1.

III. D ATASET DESCRIPTION

Each TVIR is stored in a Matlab file. BCH1_001.mat (resp. 002.mat , 003.mat and 004.mat ) contains the TVIR measured at the hydrophone located 3 m (resp. 4 m, 5 m and 6 m) below the sea surface. The sampling frequency along the delay and the time axis are fs_tau=20 kHz and fs_t=9.78 Hz, respectively. Note that the relative energy between the channels stored in the Matlab files is the same as what was measured during the experiment.

1

Time synchronization between hydrophones is also preserved in the

1

For instance, the total energy received at the third hydrophone is 8.7 dB lower than at the first hydrophone.

Fig. 1. Brest harbor experiment.

channel files. Various representations of the measured channels are shown next. They show estimates of (see [3, pp 24] for more details):

the time-varying squared magnitude,

the spreading function,

the residual phase of the main multipath arrivals (within 10 dB of the strongest path),

the power delay profile,

the autocorrelation function.

All representations plotted in logarithmic scale are given in decibels relative to their maximum value.

R EFERENCES

[1] P. van Walree, R. Otnes, and T. Jenserud, “Watermark : A realistic benchmark for underwater acoustic modems,” in Proceeding of Ucomms, Aug. 2016.

[2] F-X. Socheleau, A. Pottier, and C. Laot, “Stochastic Replay of SIMO Underwater Acoustic Communication Channels,” OCEANS 2015, pp. 1–

6, October 2015.

[3] P. van Walree, “Channel sounding for acoustic communications: tech-

niques and shallow-water examples,” Research report 2011/00007,

Norwegian Defence Research Establishment (FFI), 2011.

(3)

2

Fig. 2. Channel 1 (BCH1_001.mat).

Fig. 3. Channel 2 (BCH1_002.mat).

(4)

3

Fig. 4. Channel 3 (BCH1_003.mat).

Fig. 5. Channel 4 (BCH1_004.mat).

Références

Documents relatifs

For this data article, cloud-free satellite “Vegetation Indices 16-Day Global 250 m” Terra (MOD13Q1) and Aqua (MYD13Q1) products derived from the Moderate Resolution

The raw data consist of pantograph voltage ( v p ) and current ( i p ), onboard filter output volt- age ( v f ) and braking rheostat current ( i r ) acquired with an autonomous

In the "Conditional Search" interface of AMDA (Figure 2), the user first performs an automated search of the time intervals when the CLUSTER spacecraft encircled the

The observing system, described in the next section, is made up of the SOCIB coastal R/V, 2 un- derwater gliders, 3 profiling floats, and 25 surface drifters, complemented

Following the methodology of existing datasets, the GAD dataset does not include the audio content of the respective data due to intellectual property rights but it includes

Specifically, FairGRecs can create, via a fully parametrized API, synthetic patients profiles, containing the same characteristics that exist in a real medical database, including

Figure 1 shows the underlying workflow, which has three major steps: (1) DBpedia Live changesets are transformed to an RDF representation, (2) relevant changes are retrieved

We explain in details the generated OmniScape dataset, which includes stereo fisheye and catadioptric images acquired from the two front sides of a motorcycle, including