Much attention has been recently devoted to the analysis of coastal altimetric waveforms. When approaching the coast, altimetric waveforms are sometimes corrupted by peaks caused by high reflective areas inside the illuminated land surfaces or by the modification of the sea state close to the shoreline. This paper introduces a new parametric model for these peaky altimetric waveforms. This model assumes that the received alti- metric waveform is the sum of a Brown echo and an asymmetric Gaussian peak. The asymmetric Gaussian peak is parameterized by a location, an amplitude, a width, and an asymmetry coefficient. A maximum-likelihood estimator is studied to estimate the Brown plus peak model parameters. The Cramér–Rao lower bounds of the model parameters are then derived providing minimum variances for any unbiased estimator, i.e., a reference in terms of estimation error. The performance of the proposed model and the resulting estimation strategy are evaluated via many simulations conducted on synthetic and real data. Results obtained in this paper show that the proposed model can be used to retrack efficiently standard oceanic Brown echoes as well as coastal echoes corrupted by symmetric or asymmetric Gaussian peaks. Thus, the Brown with Gaussian peak model is useful for analyzing altimetric measurements closer to the coast.

This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters.

The present work is dedicated to the modeling and simulation of the radar signature of raindrops within wake vortices. This is achieved through the computation of the equation of raindrop motion within the wake vortex flow. Based on the inhomogeneous distribution of raindrops within wake vortices, the radar echo model is computed for raindrops in a given resolution cell. Simulated Doppler radar signatures of raindrops within wake vortices are shown to be a potential criterion for identifying wake vortex hazards in air traffic control. The dependence of the radar signature on various parameters, including the radial resolution and antenna elevation angle, is also analyzed.

From the 80’s to today, all AIRBUS civil aircraft are equipped with electrical flight control systems (EFCS). This technology now constitutes an industrial standard for commercial applications. This allows a more sophisticated aircraft control (advanced flight laws, more available autopilot...) and the setting up of specific protection functions of the flight enveloppe. In the framework of a global aircraft optimisation, for future and upcoming programs, current research efforts are dedicated to a more easy-to-handle aircraft, more efficient and so on more environmentally-friendly, resulting in augmented EFCS availability. The industrial state of practice, for all aircraft manufacturers, is to develop high levels of hardware redundancy. Therefore several sensors (for instance three angle of attack probes, three pitot probes) provide flight parameter measurements which are necessary for the computation of the flight laws, as an example. For each of these measurements, a choice or computation is performed to provide a unique and valid value among the redundant sensors. In parallel, a monitoring is done to discard a measure in case of a failure. Both processes are called « consolidation ». The aim of the Ph.D. is to provide new detection strategies to detect a failure on each sensor (monosensor monitoring) and then to design new data fusion methods to act as the actual « consolidation » process. The main idea proposes to create « software » sensors which actually are flight parameter estimators (measured by external sensors) created thanks to other dissimilar flight parameters (in our case inertial parameters, measured by inner sensors, from a different technology). The partial least squares regression (PLS) is used to perform this estimation. Detection strategies and fusion methods are following from its properties.

From the 80’s to today, all AIRBUS civil aircraft are equipped with electrical flight control systems (EFCS). This technology now constitutes an industrial standard for commercial applications. This allows a more sophisticated aircraft control (advanced flight laws, more available autopilot...) and the setting up of specific protection functions of the flight enveloppe. In the framework of a global aircraft optimisation, for future and upcoming programs, current research efforts are dedicated to a more easy-to-handle aircraft, more efficient and so on more environmentally-friendly, resulting in augmented EFCS availability. The industrial state of practice, for all aircraft manufacturers, is to develop high levels of hardware redundancy. Therefore several sensors (for instance three angle of attack probes, three pitot probes) provide flight parameter measurements which are necessary for the computation of the flight laws, as an example. For each of these measurements, a choice or computation is performed to provide a unique and valid value among the redundant sensors. In parallel, a monitoring is done to discard a measure in case of a failure. Both processes are called « consolidation ». The aim of the Ph.D. is to provide new detection strategies to detect a failure on each sensor (monosensor monitoring) and then to design new data fusion methods to act as the actual « consolidation » process. The main idea proposes to create « software » sensors which actually are flight parameter estimators (measured by external sensors) created thanks to other dissimilar flight parameters (in our case inertial parameters, measured by inner sensors, from a different technology). The partial least squares regression (PLS) is used to perform this estimation. Detection strategies and fusion methods are following from its properties.

Many measurement results on passive intermodulation products exhibit slopes of third order intermodulation product level as a function of input level different from the classical 3 dB/dB slope. Even-integer and real values between 1 and 3 are commonly reported for telephony base station towers antennas and filters. No classical model has been able to approximate these measurements up to now. We propose a non-analytic model that explains this behaviour and may serve as theoretical basis to find a physical model.

Most nonlinear behavioural models of amplifiers are based on functions that are analytic at the origin and thus can be replaced by their Taylor series development around this point, e.g. polynomials of the input signal. Chebyshev Transforms can be used to compute the harmonic response of the model to a sine input signal. These responses are polynomials of the input signal amplitude. A second application of the Chebyshev transform to the first harmonic response or RF characteristic will lend the carriers and intermodulation (IM) products for a 2-carrier input signal, again polynomials. An important class of non-analytic nonlinear behaviour encountered in practice, such as hard limiters and detectors are either empirically treated or only approximated by an analytic function such as the hyperbolic tangent. This work proposes to generalize the polynomial nonlinearity theory by adding non-analytic at the origin functions that, like polynomials, are invariant elements of the Chebyshev Transform. Devices modelled with these non-analytic at the origin functions exhibit intermodulation behaviour significantly different from that of classical polynomial models, giving theoretical foundation to a number of important unexplained practical measurement observations.

Wideband radars are promising systems that may provide numerous advantages, like simultaneous detection of slow and fast moving targets, high range-velocity resolution classification, and electronic counter-countermeasures. Unfortunately, classical processing algorithms are challenged by the range-migration phenomenon that occurs then for fast moving targets. We propose a new approach where the range migration is used rather as an asset to retrieve information about target velocities and, subsequently, to obtain a velocity dealiased mode. More specifically three new complex spectral estimators are devised in case of a single low-PRF (pulse repetition frequency) wideband waveform. The new estimation schemes enable one to decrease the level of sidelobes that arise at ambiguous velocities and, thus, to enhance the discrimination capability of the radar. Synthetic data and experimental data are used to assess the performance of the proposed estimators.