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).

In this paper, we address the problem of characterization of the GNSS pseudorange error in urban canyons. The goal of this work is to study the reliability of predicting the observation bias with a 3D GNSS simulation model. Comparison between simulated and real pseudorange bias allows the evaluation of the realism of the simulation model. We use the 3D multipath simulations into a mathematical reconstruction of the correlation function to estimate the bias at the output of the code tracking step, which provides an estimation of pseudorange error. The characteristics of the used 3D software, the collected real data and the comparison results are presented. We finish the paper by discussing different possibilities of integrating this kind of 3D city model inside the receiver processing. BIOGRAPHIES

In a MIMO radar network the multiple transmit elements may emit waveforms that differ on power and bandwidth. In this paper, we are asking, given that these two resources are limited, what is the optimal power, optimal bandwidth and optimal joint power and bandwidth allocation for best localization of multiple targets. The well known Cramer-Rao lower bound for target localization accuracy is used as a figure of merit and approximate solutions are found by minimizing a sequence of convex problems. Their quality is assessed through extensive numerical simulations and with the help of a lowerbound on the true solution. Simulations results reveal that bandwidth allocation policies have a definitely stronger impact on performance than power.

Non-analytic behavioral models of passive non linearity presented in 2013 are used to define and propose new specifications for multicarrier passive intermodulation in antennas. A margin of up to 10 dB is obtained in some cases and can be used to relax 2-carrier PIM specifications.

This paper introduces a redundancy adaptation algorithm based on an on-the-fly erasure network coding scheme named Tetrys in the context of real-time videotransmission. The algorithm exploits the relationship between the redundancy ratio used by Tetrys and the gain or loss in encoding bit rate from changing a video quality parameter called the Quantization Parameter (QP). Our evaluations show that with equal or less bandwidth occupation, the video protected by Tetrys with redundancy adaptation algorithm obtains a PSNR gain up to or more than 4 dB compared to the video without Tetrys protection. We demonstrate thatt he Tetrys redundancy adaptation algorithm performs well with the variations of both loss pattern and delay induced by the networks. We also show that Tetrys with the redundancy adaptation algorithm outperforms traditional block-based FEC codes with and without redundancy adaptation.

In this letter, we consider two sets of observations defined as subspace signals embedded in noise and we wish to analyze the distance between these two subspaces. The latter entails evaluating the angles between the subspaces, an issue reminiscent of the well-known Procrustes problem. A Bayesian approach is investigated where the subspaces of interest are considered as random with a joint prior distribution (namely a Bingham distribution), which allows the closeness of the two subspaces to be parameterized. Within this framework, the minimum mean-square distance estimator of both subspaces is formulated and implemented via a Gibbs sampler. A simpler scheme based on alternative maximum a posteriori estimation is also presented. The new schemes are shown to provide more accurate estimates of the angles between the subspaces, compared to singular value decomposition based independent estimation of the two subspaces.