This paper presents a nonlinear mixing model for joint hyperspectral image unmixing and nonlinearity detection. The proposed model assumes that the pixel reflectances are linear combinations of known pure spectral components corrupted by an additional nonlinear term, affecting the end members and contaminated by an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. The performance of the proposed strategy is first evaluated on synthetic data. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.

Satellite navigation (GNSS) is a constant in our days. The number of applications that depend on it is already remarkable and is constantly increasing. With new applications, new challenges have also risen: much of the new demand for signals comes from urban areas where GNSS signal processing is highly complex. In this thesis the issue of weak GNSS signal processing is addressed, in particular at the first phase of the receiver processing, known as signal acquisition. The first axe of research pursued deals with the analysis and compensation of the Doppler effect in acquisition. The Doppler shift that is experienced by a user is one of the main design drivers for the acquisition module and solutions are proposed to improve the sensitivity-complexity trade-off typical of the acquisition process. The second axe of research deals with the characterization of differential GNSS detectors. After a first step of coherent integration, transition to postcoherent (noncoherent or differential) integration is required for acquiring weak signals. The quantification of the sensitivity of differential detectors was not found in literature and is the objective of this part of the research. Finally, the third axe of research is devoted to multi-constellation Collective Detection (CD). CD is an innovative approach for the simultaneous processing of all signals in view. Several issues related to CD are addressed, including the improvement of the CD search process and the hybridization with standard acquisition. Finally, the application of this methodology in the context of a multi-constellation receiver is also addressed, by processing simultaneously real GPS and Galileo signals.

Satellite navigation (GNSS) is a constant in our days. The number of applications that depend on it is already remarkable and is constantly increasing. With new applications, new challenges have also risen: much of the new demand for signals comes from urban areas where GNSS signal processing is highly complex. In this thesis the issue of weak GNSS signal processing is addressed, in particular at the first phase of the receiver processing, known as signal acquisition. The first axe of research pursued deals with the analysis and compensation of the Doppler effect in acquisition. The Doppler shift that is experienced by a user is one of the main design drivers for the acquisition module and solutions are proposed to improve the sensitivity-complexity trade-off typical of the acquisition process. The second axe of research deals with the characterization of differential GNSS detectors. After a first step of coherent integration, transition to postcoherent (noncoherent or differential) integration is required for acquiring weak signals. The quantification of the sensitivity of differential detectors was not found in literature and is the objective of this part of the research. Finally, the third axe of research is devoted to multi-constellation Collective Detection (CD). CD is an innovative approach for the simultaneous processing of all signals in view. Several issues related to CD are addressed, including the improvement of the CD search process and the hybridization with standard acquisition. Finally, the application of this methodology in the context of a multi-constellation receiver is also addressed, by processing simultaneously real GPS and Galileo signals.

Présentatrion des apports des techniques avancées de Traitement du Signal pour les PMEs

Nowadays, the majority of new GNSS applications targets dynamic users in urban environments; therefore the decoder input in GNSS receivers needs to be adapted to the urban propagation channel to avoid mismatched decoding when using soft input channel decoding. The aim of this paper consists thus in showing that the GNSS signals demodulation performance is significantly improved integrating an advanced soft detection function as decoder input in urban areas. This advanced detection function takes into account takes into account some a priori information on the available Channel State Information (CSI). If no CSI is available, one has to blindly adapt the detection function in order to operate close to the perfect CSI case. This will lead to avoid mismatched decoding due to, for example, the consideration by default of the Additive White Gaussian Noise (AWGN) channel for the derivation of soft inputs to be fed to soft input decoders. As a consequence the decoding performance will be improved in urban areas. The expressions of the soft decoder input function adapted for an urban environment is highly dependent on the available CSI at the receiver end. Based on different model of urban propagation channels, several CSI contexts will be considered namely perfect CSI, partial statistical CSI and no CSI. Simulation results will be given related to the GPS L1C demodulation performance with these different advanced detection function expressions in an urban environment. The results presented in this paper are valid for any kind of soft input decoders, such as Viterbi decoding for trellis based codes, the MAP/BCJR decoding for turbo-codes and the Belief Propagation decoding for LDPC codes.

This paper deals with the demodulation of automatic identification system (AIS) signals received by a satellite. More precisely, an error correction algorithm is presented, whose computational complexity is reduced with respect to that of a previously considered approach. This latter approach makes use of the cyclic redundancy check (CRC) of a message as redundancy, in order to correct transmission errors. In this paper, the CRC is also considered as a correction tool, but only a part of it is used for that purpose; the remaining part is only used as an error detection means. This novel approach allows the decoding performance to be adapted to the noise power, and provides a reduction of the computational complexity. Simulation results obtained with and without complexity optimization are presented and compared in the context of the AIS system.

This paper proposes a method that simultaneously builds the analytic signal from non-uniform samples of a bandpass signal and rejects interferences. The analytic signal is required for many onboard operations in communication satellites. This method operates in the time domain and without preliminary demodulation, using Periodic Non-uniform Sampling of order 2 (PNS2). This non-uniform sampling scheme can be easily implemented with available devices. Exact formulas for the analytic signal construction are derived for an infinite observation window (an infinite number of samples). For practical applications, the formulas should also demonstrate a high convergence rate due to the finite observation window. Formulas with increasing convergence rates are thus derived. The proposed method has been tested through simulations according to the number of available samples, the interference parameters and the filter transfer function regularity.

This paper introduces a new statistical model for homogeneous images acquired by the same kind of sensor (e.g., two optical images) and heterogeneous images acquired by different sensors (e.g., optical and synthetic aperture radar (SAR) images). The proposed model assumes that each image pixel is distributed according to a mixture of multi-dimensional distributions depending on the noise properties and on the transformation between the actual scene and the image intensities. The parameters of this new model can be estimated by the classical expectation-maximization algorithm. The estimated parameters are finally used to learn the relationships between the different images. This information can be used in many image processing applications, particularly those requiring a similarity measure (e.g., change detection or registration). Simulation results on synthetic and real images show the potential of the proposed model. A brief application to change detection between optical and SAR images is finally investigated.

In the analysis of fluctuating clutter in radar systems, the dc/ac ratio, defined as the ratio of power contained in the dc component at zero Doppler to the power contained in the clutter spectrum, is an important parameter which impacts the detection of slowly-moving targets. Thus, proper characterization of the dc/ac ratio is important for a meaningful assessment of system performance. We explain why the dc component can be hidden due to shortness of the data set or due to apodization. We show that a random propagation time can explain any form of dc/ac ratio and many shapes of the broadband Doppler spectra. Examples are based on a paper by Narayanan et al. about backscatter from trees of a radar at 8 GHz.

Wideband radar systems are highly resolved in range, which is a desirable feature for mitigating clutter. However, due to a smaller range resolution cell, moving targets are prone to migrate along the range during the coherent processing interval (CPI). This range walk, if ignored, can lead to huge performance degradation in detection. Even if compensated, conventional processing may lead to high sidelobes preventing from a proper detection in case of a multitarget scenario. Turning to a compressed sensing framework, we present a Bayesian algorithm that gives a sparse representation of migrating targets in case of a wideband waveform. Particularly, it is shown that the target signature is the sub-Nyquist version of a virtually well-sampled two-dimensional (2D)-cisoid. A sparse-promoting prior allows then this cisoid to be reconstructed and represented by a single peak without sidelobes. Performance of the proposed algorithm is finally assessed by numerical simulations on synthetic and semiexperimental data. Results obtained are very encouraging and show that a nonambiguous detection mode may be obtained with a single pulse repetition frequency (PRF).