This paper presents an algorithm based on sparse representation for fusing hyperspectral and multispectral images. The observed images are assumed to be obtained by spectral or spatial degradations of the high resolution hyperspectral image to be recovered. Based on this forward model, the fusion process is formulated as an inverse problem whose solution is determined by optimizing an appropriate criterion. To incorporate additional spatial information within the objective criterion, a regularization term is carefully designed, relying on a sparse decomposition of the scene on a set of dictionaries. The dictionaries and the corresponding supports of active coding coefficients are learned from the observed images. Then, conditionally on these dictionaries and supports, the fusion problem is solved by iteratively optimizing with respect to the target image (using the alternating direction method of multipliers) and the coding coefficients. Simulation results demonstrate the efficiency of the proposed fusion method when compared with the state-of-the-art.

In this paper, we address the design of a cooperative protocol for a hybrid satellite/terrestrial emergency system. We want to perform energy savings compared to the case where all the terrestrial relay nodes are forwarding satellite messages to ground receivers. This is done via the selection of relevant relay nodes. The parameterization of the protocol phases has been done through simulations and takes into account the duration of the selection process, the number of selected nodes, and the signaling overhead. The selection process based on a node identifier (ID) appears to provide greater energy savings compared to the selection process based on the signal to interference and noise ratio (SINR). The solutions have been implemented in the real case scenario of forest fire that has been thoroughly documented by the US administration. According to the scenario parameters, 100% of the masked nodes are reached after cooperation.

This article addresses the problem of signal reconstruction, spectral estimation and linear filtering directly from irregularly-spaced samples of a continuous signal (or autocorrelation function in the case of random signals) when signal spectrum is assumed to be bounded. The number 2L of samples is assumed to be large enough so that the variation of the spectrum on intervals of width π/L is small. Reconstruction formulas are based on PNS (Periodic Nonuniform Sampling) schemes. They allow for reconstruction schemes not requiring regular resampling and suppress two stages in classical computations. The presented method can also be easily generalized to spectra in symmetric frequency bands (bandpass signals).

In this paper, we derive an asymptotic analysis for a capacity approaching design of serially concatenated turbo schemes that works for both systematic generalized irregular repeat accumulate (GIRA) and low density generator matrix (LDGM) codes concatenated with a continuous phase modulation (CPM). The proposed design is based on a semi analytic EXIT chart optimization method. By considering a particular scheduling, inserting partial interleavers between GIRA accumulator and CPM and allowing degree-1 variable nodes, we show that designed rates perform very close to the maximum achievable rate (R∗).

A powerful approach to sparse representation, dictionary learning consists in finding a redundant frame in which the representation of a particular class of images is sparse. In practice, all algorithms performing dictionary learning iteratively estimate the dictionary and a sparse representation of the images using this dictionary. However, the numerical complexity of dictionary learning restricts its use to atoms with a small support. A way to alleviate these issues is introduced in this paper, consisting in dictionary atoms obtained by translating the composition of K convolutions with S-sparse kernels of known support. The dictionary update step associated with this strategy is a non-convex optimization problem, which we study here. A block-coordinate descent or Gauss-Seidel algorithm is proposed to solve this problem, whose search space is of dimension KS, which is much smaller than the size of the image. Moreover, the complexity of the algorithm is linear with respect to the size of the image, allowing larger atoms to be learned (as opposed to small patches). An experiment is presented that shows the approximation of a large cosine atom with K = 7 sparse kernels, demonstrating a very good accuracy.

The concept of delay/Doppler (DD) altimetry (DDA) has been under study since the mid-1990s, aiming at reducing the measurement noise and increasing the along-track resolution in comparison with the conventional pulse-limited altimetry. This paper introduces a new model for the mean backscattered power waveform acquired by a radar altimeter operating in synthetic aperture radar mode, as well as an associated least squares (LS) estimation algorithm. As in conventional altimetry (CA), the mean power can be expressed as the convolution of three terms: the flat surface impulse response (FSIR), the probability density function of the heights of the specular scatterers, and the time/frequency point target response of the radar. An important contribution of this paper is to derive an analytical formula for the FSIR associated with DDA. This analytical formula is obtained for a circular antenna pattern, no mispointing, no vertical speed effect, and a uniform scattering. The double convolution defining the mean echo power can then be computed numerically, resulting in a 2-D semi-analytical model called the DD map (DDM). This DDM depends on three altimetric parameters: the epoch, the sea surface wave height, and the amplitude. A multi-look model is obtained by summing all the reflected echoes from the same along-track surface location of interest after applying appropriate delay compensation (range migration) to align the DDM on the same reference. The second contribution of this paper concerns the estimation of the parameters associated with the multi-look semi-analytical model. An LS approach is investigated by means of the Levenberg–Marquardt algorithm. Simulations conducted on simulated altimetric waveforms allow the performance of the proposed estimation algorithm to be appreciated. The analysis of Cryosat-2 waveforms shows an improvement in parameter estimation when compared to the CA.

This paper introduces a new model for delay/Doppler altimetry, taking into account the effect of antenna mispointing. After defining the proposed model, the effect of the antenna mispointing on the waveform is analyzed with respect to along-track and across-track directions. Two least squaresapproaches are proposed for the estimation of the altimetric parameters. The first algorithm estimates four parameters including the across-track mispointing (which affects the echo’s shape) while the second algorithm considers the mispointing angles provided by the star-trackers and estimates the three remaining parameters. The proposed model and algorithms are validated via simulations conducted on both synthetic and real data.

In this paper, we derive an asymptotic analysis and optimization of coded CPM systems using both unstructured and protograph-based LDPC codes ensembles. First, we present a simple yet effective approach to design unstructured LDPC codes: by inserting partial interleavers between LDPC and CPM, and allowing degree-1 and degree-2 variable nodes in a controlled pattern, we show that designed codes perform that can operate very close to the maximum achievable rates. Finally, the extension to protograph based codes is discussed. We provide some simple rules to design good protograph codes with good threshold properties.

The propagation of a laser beam through the atmosphere leads to spectral widening, which has a detrimental effect on information transmission. In literature the study of laser beam envelope and phase was achieved through demodulation. In this paper we explain that amplitude demodulation in the baseband leads to changes in analytic signals. We give a formula which allows this drawback to be overcome, achieving two demodulations with same frequency and different phases. Examples are given in the Gaussian framework and in the case of random propagation times.

This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are post-nonlinear functions of unknown pure spectral components contaminated by an additive white Gaussian noise. These nonlinear functions are approximated using second-order polynomials leading to a polynomial postnonlinear mixing model. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding an unsupervised nonlinear unmixing algorithm. Due to the large number of parameters to be estimated, an efficient Hamiltonian Monte Carlo algorithm is investigated. The classical leapfrog steps of this algorithm are modified to handle the parameter constraints. The performance of the unmixing strategy, including convergence and parameter tuning, is first evaluated on synthetic data. Simulations conducted with real data finally show the accuracy of the proposed unmixing strategy for the analysis of hyperspectral images.