2012
DOI: 10.1117/12.975001
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Time/frequency coding for Brillouin distributed sensors

Abstract: In this paper, we propose a novel coding for long range Brillouin Optical Time Analysis (BOTDA) distributed sensors based on a combination of time and frequency pulses, resulting in an additional coding gain of √2 with respect to traditional intensity-modulated codes. The generation of frequency-chirped pseudo-arbitrary pulses in return-to-zero (RZ) format with a Direct-Digital Synthesizer (DDS) is presented and the coding gain is experimentally verified, perfectly matching its theoretical value.

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Cited by 17 publications
(21 citation statements)
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“…While coding schemes based on incoherent pulse compression [19,20] have efficiently been used in high spatial resolution sensors, demonstrations in long-range BOTDA have been classically based on unipolar sequences, such as Simplex [9] or Golay [11] codes. Two kinds of pulse coding schemes have been recently proposed as a new breach in long range sensing: bipolar codes [12] and time/frequency or colored codes [13][14][15]. Both coding schemes offer improved SNR enhancement in comparison to traditionally-used unipolar coding schemes [9][10][11].…”
Section: Introductionmentioning
confidence: 99%
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“…While coding schemes based on incoherent pulse compression [19,20] have efficiently been used in high spatial resolution sensors, demonstrations in long-range BOTDA have been classically based on unipolar sequences, such as Simplex [9] or Golay [11] codes. Two kinds of pulse coding schemes have been recently proposed as a new breach in long range sensing: bipolar codes [12] and time/frequency or colored codes [13][14][15]. Both coding schemes offer improved SNR enhancement in comparison to traditionally-used unipolar coding schemes [9][10][11].…”
Section: Introductionmentioning
confidence: 99%
“…Besides enhancing BOTDA systems for long range [9][10][11][12][13][14][15], optical pulse coding has also be applied to high spatial resolution Brillouin sensors, based for instance in correlation-domain [19]. While coding schemes based on incoherent pulse compression [19,20] have efficiently been used in high spatial resolution sensors, demonstrations in long-range BOTDA have been classically based on unipolar sequences, such as Simplex [9] or Golay [11] codes.…”
Section: Introductionmentioning
confidence: 99%
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“…The signal-to-noise ratio (SNR) of Brillouin optical timedomain analysers (BOTDA) has been substantially improved using advanced techniques, such as distributed Raman amplification 2 , optical pulse coding 3,4 or other kinds of signal processing 5 , especially when those methods are combined in a single system 6 . Among the different methods proposed in the literature, signal processing techniques, such as optical pulse coding 3,4 and wavelets 5 , have demonstrated to be very efficient tools to remove noise from a unidimensional array of data; and therefore, their use in BOTDA sensing requires processing time-domain traces at each scanned pump-probe frequency offset independently from each other. A 3D map of the Brillouin gain spectrum (BGS) versus distance can thus be obtained with an improved SNR after processing each time-domain trace [3][4][5] .…”
Section: Introductionmentioning
confidence: 99%
“…Among the different methods proposed in the literature, signal processing techniques, such as optical pulse coding 3,4 and wavelets 5 , have demonstrated to be very efficient tools to remove noise from a unidimensional array of data; and therefore, their use in BOTDA sensing requires processing time-domain traces at each scanned pump-probe frequency offset independently from each other. A 3D map of the Brillouin gain spectrum (BGS) versus distance can thus be obtained with an improved SNR after processing each time-domain trace [3][4][5] . Although methods such as time-frequency codes 4 take advantage of the double scanning (fibre position and pump-probe frequency detuning) required in a BOTDA sensor, the SNR enhancement provided by that method is basically given by the capability of the code to reduce noise in a unidimensional array of data.…”
Section: Introductionmentioning
confidence: 99%