Dans les systèmes de transmission multiporteuses et impliquant plusieurs utilisateurs, deux phénomènes viennent perturber la réception et la détection de symboles : le canal de propagation et le décalage des fréquences porteuses (DFP). Cette thèse traite de techniques d’égalisation et de synchronisation en fréquence reposant sur des techniques de type Kalman telles que le filtrage de Kalman étendu (EKF) du 1er ou du 2nd ordre, le filtrage de Kalman étendu itératif ou le filtrage de Kalman par sigma point (SPKF). Pour relaxer les hypothèses de Gaussianité sur les bruits de mesure et de modèle dans la représentation dans l’espace d’état, des approches de type H[infini] sont aussi étudiées.Ces méthodes sont ensuite exploitées dans des systèmes de type OFDMA ou OFDM-IDMA et sont combinées avec d’autres approches (MMSE-SD, tests statistiques, etc.) pour mettre en œuvre des récepteurs pouvant être notamment robustes à des interférences large bande, comme c’est le cas dans des applications de radio intelligence. / Multicarrier modulation is the common feature of high-data rate mobile wirelesssystems. In that case, two phenomena disturb the symbol detection. Firstly,due to the relative transmitter-receiver motion and a difference between the localoscillator (LO) frequency at the transmitter and the receiver, a carrier frequencyoffset (CFO) affects the received signal. This leads to an intercarrier interference(ICI). Secondly, several versions of the transmitted signal are received due to thewireless propagation channel. These unwanted phenomena must be taken intoaccount when designing a receiver. As estimating the multipath channel and theCFO is essential, this PhD deals with several CFO and channel estimation methodsbased on optimal filtering.Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalmanfilter (EKF). It is based on a local linearization of the equations around the laststate estimate. However, this approach requires a linearization based on calculationsof Jacobians and Hessians matrices and may not be a sufficient descriptionof the nonlinearity. For these reasons, we can consider the sigma-point Kalmanfilter (SPKF), namely the unscented Kalman Filter (UKF) and the central differenceKalman filter (CDKF). The UKF is based on the unscented transformationwhereas the CDKF is based on the second order Sterling polynomial interpolationformula. Nevertheless, the above methods require an exact and accurate apriori system model as well as perfect knowledge of the additive measurementnoisestatistics. Therefore, we propose to use the H∞ filtering, which is known tobe more robust to uncertainties than Kalman filtering. As the state-space representationof the system is non-linear, we first evaluate the “extended H∞ filter”,which is based on a linearization of the state-space equations like the EKF. As analternative, the “unscented H∞ filter”, which has been recently proposed in theliterature, is implemented by embedding the unscented transformation into the“extended H∞ filter” and carrying out the filtering by using the statistical linearerror propagation approach.The above techniques have been implemented in different multicarrier contexts:Firstly, we address the estimation of the multiple CFOs and channels by meansof a control data in an uplink orthogonal frequency division multiple access(OFDMA) system. To reduce the amount of control data, the optimal filteringtechniques are combined in an iterative way with the so-called minimum meansquare error successive detector (MMSE-SD) to obtain an estimator that doesnot require pilot subcarriers.