Acoustic impulse response mapping for acoustic communications in shallow water

Caimi, Frank M.; Tongta, Rangsan; Carroll, M. M.; Murshid, Syed H.

doi:10.1109/oceans.1998.726385

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Recently, researchers at the Harbor Branch Oceanographic Institution in Florida suggested an alternative approach based on system identification [Caimi98a,Caimi98b]. The channel impulse response is explicitly estimated and the transmitted symbols are then recovered from the received signal via deconvolution.…”

Section: Equalization Via System Identificationmentioning

confidence: 99%

Underwater Acoustic Data Communications for Autonomous Platform Command, Control and Communications

Shinego¹,

Edelson²,

Menas³

et al. 2001

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Section: Equalization Via System Identificationmentioning

confidence: 99%

Underwater Acoustic Data Communications for Autonomous Platform Command, Control and Communications

Shinego¹,

Edelson²,

Menas³

et al. 2001

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“…Also, the use of DEE [1] and SID [2,3] methods has been reported to produce acceptable results under specific conditions. However, it is rarely used in ACOMMs because the computations required for the MLSE grow exponentially with delay of the channel.…”

Section: Introductionmentioning

confidence: 99%

<title>Results using an alternative approach to channel equalization using a pattern classification strategy</title>

Caimi

Hassan

1999

SPIE Proceedings

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In underwater acoustic communication, (ACOMMs), ocean surface and bottom conditions create multi path propagation's for the transmitted signal that result in Inter symbol Interference (ISI) at the receiver. Generally, Equalization, Diversity IBeam forming, and Channel Coding are three independent techniques that are used to improve received signal quality. Equalization compensates for ISI created by a band-limited, time-dispersive channel through implementation of specialized filtering schemes within the receiver. The coefficients of the equalizer need to be continuously adjusted to compensate for the variability in the channel. Since the number of states required by the equalizer (or beam former) is finite, and may be described over a limited set of operating conditions (environments), it is worthwhile to consider a pattern recognition approach for identifying channel conditions and subsequent equalizer state specifications. This paper describes the approach and preliminary results obtained form the use of pattern recognition techniques to select a set of "best choice" equalizer coefficients and to decode a signal sequence directly. The method does not rely on the application of any adaptive algorithm for estimation of the equalizer coefficients during the actual data transmission or reception.Expectations are that performance benefits may be gamed in those cases where adaptive algorithms fail to select the optimal filter coefficients due to computational complexity or other factors.

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