Estimation of the underwater acoustic channel

This paper deals with the processing of real data recorded during the Oceanic Acoustic Tomography experiments « PASSTIME » carried out by the SHOM in October 2005. The transmitted signal waveform of interest is assumed to be known at the receiver side, and we address the problem of the channel impulse response estimation in a bayesian framework : the channel is modeled via a Bernoulli-Gaussian process, and estimation is achieved via a Gibbs sampler. As the processing of PASSTIME data emphasizes distorsions of the transmitted signal, we add a preliminary processing step for estimating the distorded waveform by means of a simple iterative procedure that alternatively estimates the channel impulse response via a Matching Pursuit approach, and the waveform itself by maximizing the likelihood. The proposed algorithms are applied to PASSTIME records, and prove to be very satisfactory.Dans cet article, nous nous intéressons au traitement des données réelles enregistrées au cours de la campagne de Tomographie Acoustique Océanique « PASSTIME » effectuée par le SHOM en Octobre 2005. Pour une forme d'onde transmise connue du récepteur, nous proposons de présenter le problème de l'estimation de la réponse impulsionnelle du canal acoustique sous-marin sous la forme d'un problème bayésien. Le canal est alors modélisé par un a priori Bernoulli-Gaussien, et l'estimateur est évalué à partir de simulations générées au moyen d'un algorithme de Gibbs. Comme le traitement des données PASSTIME met en évidence d'importantes distorsions subies par le signal, nous effectuons l'estimation préalable de la forme d'onde distordue au moyen d'un algorithme alternant l'estimation du canal par une méthode de type Matching Pursuit, et l'estimation de la forme d'onde au sens du maximum de vraisemblance. L'application aux données PASSTIME conduit à des résultats très satisfaisants

Joint synchronization of clock phase offset, skew and drift in reference broadcast synchronization (RBS) protocol

Time-synchronization in wireless ad-hoc sensor networks is a crucial piece of infrastructure. Thus, it is a fundamental design problem to have a good clock syn- chronization amongst the nodes of wireless ad-hoc sensor networks. Motivated by this fact, in this thesis, the joint maximum likelihood (JML) estimator for relative clock phase offset and skew under the exponential noise model for the reference broadcast synchronization protocol is formulated and found via a direct algorithm. The Gibbs Sampler is also proposed for joint estimation of relative clock phase offset and skew, and shown to provide superior performance compared to the JML-estimator. Lower and upper bounds for the mean-square errors (MSE) of the JML-estimator and the Gibbs Sampler are introduced in terms of the MSE of the uniform minimum variance unbiased estimator and the conventional best linear unbiased estimator, respectively. The suitability of the Gibbs Sampler for estimating additional unknown parameters is shown by applying it to the problem in which synchronization of clock drift is also needed