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National audience; In the STAP domain, modeling the interferences as an autoregressive (AR) process with the detector called Parametric Adaptive Matched Filter (PAMF), provides an estimation of the clutter-rejection filter with few training data. The main difficulty of this approach is the estimation of the AR matrices by using the training data. Thus, we propose an on-line estimation based on the Kalman filter and its variants. A comparative study is carried out and illustrates the relevance of such approaches with data provided by the DGA.

Recent studies in human health assume a causal link between the complexity of psychophysiological control systems and the complexity of their resulting biosignals. This PhD illustrates the aforementioned principle by relying on an interdisciplinary approach, combining physiology, psychology and signal processing. The dynamics of human output physiological signals are studied in response to induced stress in individual or collective situations. The objective is to extract individual signatures depicting the central and autonomic regulations at rest or in different experimental situations. Since stress is a multifactorial process depending on the individual perception and interpretation of a situation, the study of physiological signals is combined with the evaluation of psychological contextual and dispositional characteristics. We focus our attention on cardiac regulations which are analysed from the time series defined by the successive durations of the RR intervals. Statistical signal processing methods, either temporal, frequency or non-linear, are used to study the adaptive capacities of individuals facing different situations of cognitive tasks associated or not with stressors. A particular interest is given to multiscale entropy to assess the complexity of signals, which makes it possible to consider the interconnections existing between cortical, subcortical structures and autonomic cardiac regulations. The probability density functions of recorded cardiac signals along each different experimental situation are compared two by two by using the Kullback-Leibler divergence, and in particular the estimate of the asymptotic increment of the divergence of Kullback-Leibler. The results show that studying cardiac signals allows to discriminate the psychophysiological state of an individual when facing either cognitive tasks or stressful situations. Psychophysiological state differences emerge during stress, not only at an individual level, but also at a collective one, for which the subject is not directly confronted with stressful stimuli. The stress is therefore empathic. Two experimental applications are carried out from our results. First, we show that the cardiac complexity, which is altered in people stressed at work, can be improved by cardiac coherence biofeedback training. Second, signal processing methods are also used to the study of postural regulation. Overall, our results strengthen the interest of human monitoring in health.; Les études récentes en santé humaine supposent un lien de causalité entre la complexité des systèmes de contrôle psychophysiologique et la complexité des biosignaux qu’ils émettent. Le travail mené dans le cadre de cette thèse illustre ce principe en s’appuyant sur une démarche interdisciplinaire, combinant physiologie, psychologie et traitement du signal. Il vise à étudier les dynamiques des signaux physiologiques émis par l’Homme, en réponse à un stress induit en situation individuelle ou collective. Le stress étant un processus multifactoriel qui dépend de la perception et de l’interprétation d’une situation donnée par un individu, l’étude des signaux physiologiques est combinée à l’évaluation de caractéristiques psychologiques contextuelles et dispositionnelles. En particulier, nous nous intéressons aux régulations cardiaques qui sont analysées à partir des séries temporelles définies par les durées successives des intervalles RR. Des approches statistiques temporelles, fréquentielles ou non-linéaires sont utilisées afin d’étudier les capacités d’adaptation des individus confrontés à différentes situations de tâches cognitives associées ou non à des facteurs stressants. Il s’agit d’extraire des signatures caractéristiques des régulations centrales et autonomes, au repos ou dans différentes situations expérimentales. Dans ce travail, un intérêt particulier est accordé à l’entropie multi-échelles afin d’évaluer la complexité des signaux, une complexité induite par les interconnexions existant entre structures corticales, sous-corticales et régulations autonomes cardiaques. Nous proposons également d’analyser les signaux collectés durant les différentes situations expérimentales, en comparant deux à deux leurs densités de probabilité à partir de la divergence de Kullback-Leibler, et en particulier d’une estimation de l'incrément asymptotique de la divergence de Kullback-Leibler. Les résultats obtenus mettent en évidence que l’étude des signaux cardiaques peut permettre d’appréhender l’état psychophysiologique d’un individu lorsqu’il est confronté à des situations de tâches cognitives et de stress. Des différences d’états apparaissent non seulement à l’échelle individuelle, mais également à l’échelle collective, lorsque l’individu n’est pas directement confronté aux stimuli stressants mais que le stress est de nature empathique. Enfin, deux applications sont réalisées. Nous montrons que la complexité des signaux cardiaques, altérée chez des personnes stressées au travail, peut être améliorée par un entraînement à la cohérence cardiaque. Nous appliquons également les méthodes de traitement du signal à l’étude de la régulation posturale. L’ensemble de nos résultats renforcent l’intérêt du monitoring de l’humain en matière de santé.

Multicarrier modulation is the common feature of high-data rate mobile wireless systems. In that case, two phenomena disturb the symbol detection. Firstly, due to the relative transmitter-receiver motion and a difference between the local oscillator (LO) frequency at the transmitter and the receiver, a carrier frequency offset (CFO) affects the received signal. This leads to an intercarrier interference (ICI). Secondly, several versions of the transmitted signal are received due to the wireless propagation channel. These unwanted phenomena must be taken into account when designing a receiver. As estimating the multipath channel and the CFO is essential, this PhD deals with several CFO and channel estimation methods based on optimal filtering. Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalman filter (EKF). It is based on a local linearization of the equations around the last state estimate. However, this approach requires a linearization based on calculations of Jacobians and Hessians matrices and may not be a sufficient description of the nonlinearity. For these reasons, we can consider the sigma-point Kalman filter (SPKF), namely the unscented Kalman Filter (UKF) and the central difference Kalman filter (CDKF). The UKF is based on the unscented transformation whereas the CDKF is based on the second order Sterling polynomial interpolation formula. Nevertheless, the above methods require an exact and accurate a priori system model as well as perfect knowledge of the additive measurementnoise statistics. Therefore, we propose to use the H∞ filtering, which is known to be more robust to uncertainties than Kalman filtering. As the state-space representation of 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 an alternative, the “unscented H∞ filter”, which has been recently proposed in the literature, is implemented by embedding the unscented transformation into the “extended H∞ filter” and carrying out the filtering by using the statistical linear error propagation approach. Multicarrier modulation is the common feature of high-data rate mobile wireless systems. In that case, two phenomena disturb the symbol detection. Firstly, due to the relative transmitter-receiver motion and a difference between the local oscillator (LO) frequency at the transmitter and the receiver, a carrier frequency offset (CFO) affects the received signal. This leads to an intercarrier interference (ICI). Secondly, several versions of the transmitted signal are received due to the wireless propagation channel. These unwanted phenomena must be taken into account when designing a receiver. As estimating the multipath channel and the CFO is essential, this PhD deals with several CFO and channel estimation methods based on optimal filtering. Firstly, as the estimation issue is nonlinear, we suggest using the extended Kalman filter (EKF). It is based on a local linearization of the equations around the last state estimate. However, this approach requires a linearization based on calculations of Jacobians and Hessians matrices and may not be a sufficient description of the nonlinearity. For these reasons, we can consider the sigma-point Kalman filter (SPKF), namely the unscented Kalman Filter (UKF) and the central difference Kalman filter (CDKF). The UKF is based on the unscented transformation whereas the CDKF is based on the second order Sterling polynomial interpolation formula. Nevertheless, the above methods require an exact and accurate a priori system model as well as perfect knowledge of the additive measurementnoise statistics. Therefore, we propose to use the H∞ filtering, which is known to be more robust to uncertainties than Kalman filtering. As the state-space representation of 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 an alternative, the “unscented H∞ filter”, which has been recently proposed in the literature, is implemented by embedding the unscented transformation into the “extended H∞ filter” and carrying out the filtering by using the statistical linear error propagation approach.

Recent studies in human health assume a causal link between the complexity of psychophysiological control systems and the complexity of their resulting biosignals. This PhD illustrates the aforementioned principle by relying on an interdisciplinary approach, combining physiology, psychology and signal processing. The dynamics of human output physiological signals are studied in response to induced stress in individual or collective situations. The objective is to extract individual signatures depicting the central and autonomic regulations at rest or in different experimental situations. Since stress is a multifactorial process depending on the individual perception and interpretation of a situation, the study of physiological signals is combined with the evaluation of psychological contextual and dispositional characteristics. We focus our attention on cardiac regulations which are analysed from the time series defined by the successive durations of the RR intervals. Statistical signal processing methods, either temporal, frequency or non-linear, are used to study the adaptive capacities of individuals facing different situations of cognitive tasks associated or not with stressors. A particular interest is given to multiscale entropy to assess the complexity of signals, which makes it possible to consider the interconnections existing between cortical, subcortical structures and autonomic cardiac regulations. The probability density functions of recorded cardiac signals along each different experimental situation are compared two by two by using the Kullback-Leibler divergence, and in particular the estimate of the asymptotic increment of the divergence of Kullback-Leibler. The results show that studying cardiac signals allows to discriminate the psychophysiological state of an individual when facing either cognitive tasks or stressful situations. Psychophysiological state differences emerge during stress, not only at an individual level, but also at a collective one, for which the subject is not directly confronted with stressful stimuli. The stress is therefore empathic. Two experimental applications are carried out from our results. First, we show that the cardiac complexity, which is altered in people stressed at work, can be improved by cardiac coherence biofeedback training. Second, signal processing methods are also used to the study of postural regulation. Overall, our results strengthen the interest of human monitoring in health., Les études récentes en santé humaine supposent un lien de causalité entre la complexité des systèmes de contrôle psychophysiologique et la complexité des biosignaux qu’ils émettent. Le travail mené dans le cadre de cette thèse illustre ce principe en s’appuyant sur une démarche interdisciplinaire, combinant physiologie, psychologie et traitement du signal. Il vise à étudier les dynamiques des signaux physiologiques émis par l’Homme, en réponse à un stress induit en situation individuelle ou collective. Le stress étant un processus multifactoriel qui dépend de la perception et de l’interprétation d’une situation donnée par un individu, l’étude des signaux physiologiques est combinée à l’évaluation de caractéristiques psychologiques contextuelles et dispositionnelles. En particulier, nous nous intéressons aux régulations cardiaques qui sont analysées à partir des séries temporelles définies par les durées successives des intervalles RR. Des approches statistiques temporelles, fréquentielles ou non-linéaires sont utilisées afin d’étudier les capacités d’adaptation des individus confrontés à différentes situations de tâches cognitives associées ou non à des facteurs stressants. Il s’agit d’extraire des signatures caractéristiques des régulations centrales et autonomes, au repos ou dans différentes situations expérimentales. Dans ce travail, un intérêt particulier est accordé à l’entropie multi-échelles afin d’évaluer la complexité des signaux, une complexité induite par les interconnexions existant entre structures corticales, sous-corticales et régulations autonomes cardiaques. Nous proposons également d’analyser les signaux collectés durant les différentes situations expérimentales, en comparant deux à deux leurs densités de probabilité à partir de la divergence de Kullback-Leibler, et en particulier d’une estimation de l'incrément asymptotique de la divergence de Kullback-Leibler. Les résultats obtenus mettent en évidence que l’étude des signaux cardiaques peut permettre d’appréhender l’état psychophysiologique d’un individu lorsqu’il est confronté à des situations de tâches cognitives et de stress. Des différences d’états apparaissent non seulement à l’échelle individuelle, mais également à l’échelle collective, lorsque l’individu n’est pas directement confronté aux stimuli stressants mais que le stress est de nature empathique. Enfin, deux applications sont réalisées. Nous montrons que la complexité des signaux cardiaques, altérée chez des personnes stressées au travail, peut être améliorée par un entraînement à la cohérence cardiaque. Nous appliquons également les méthodes de traitement du signal à l’étude de la régulation posturale. L’ensemble de nos résultats renforcent l’intérêt du monitoring de l’humain en matière de santé.