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Conference Paper
Selective Analytic Signal Construction from a Non-Uniform Ssampled Bandpass Signal
In Proc. Int. Conf. Acoust., Speech and Signal Processing (ICASSP), Florence, Italy, May 4-9, 2014.
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.
Signal and image processing / Space communication systems
A Multivariate Statistical Model for Multiple Images Acquired by Homogeneous or Heterogeneous Sensors
In Proc. Int. Conf. Acoust., Speech and Signal Processing (ICASSP), Florence, Italy, May 4-9, 2014.
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.
Signal and image processing / Earth observation
Journal Paper
Characteristics of the DC/AC Ratio of Radar Backscatter from Trees
IEEE Transactions on Aerospace and Electronic Systems, vol. 50, Issue 1, pp. 364-370, January, 2014.
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.
Signal and image processing / Other
Bayesian Sparse Estimation of Migrating Targets for Wideband Radar
IEEE Trans. Aerosp. Electron. Syst., vol. 50, no. 2, pp. 871-886, April, 2014.
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).
Signal and image processing / Localization and navigation
Conference Paper
Prediction of GNSS Signal Bias Using a 3D Model in Urban Environments
In Proc. 17th European Navigation Conference (ENC 2013), Vienna, Austria, April 23-25, 2013.
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
Signal and image processing / Space communication systems
Journal Paper
Resource Allocation in MIMO Radar With Multiple Targets for Non-Coherent Localization
IEEE Transactions on, vol. 62, pp. 2656-2666, April, 2014.
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.
Signal and image processing / Aeronautical communication systems
Conference Paper
Relaxation of the Multicarrier Passive Intermodulation Specifications of Antennas
In Proc. European Conference on Antennas and Propagation (EuCAP), The Hague, Netherlands, April 6-11, 2014.
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.
Signal and image processing / Space communication systems
Journal Paper
Joint On-The-Fly Network Coding/Video Quality Adaptation for Real-Time Delivery
Elsevier Signal Processing : Image Communication, vol. 29, n° 4, pp 449-461, April, 2014.
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.
Networking / Space communication systems
Joint Bayesian Estimation of Close Subspaces from Noisy Measurements
IEEE Signal Processing Letters, vol. 21 n° 2, pp. 168-171, February, 2014.
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.
Signal and image processing / Other
Conference Paper
Sparse Bayesian Image Restoration with Linear Operator Uncertainties with Application to EEG Signal Recovery
In Proc. Middle East Conference on Biomedical Engineering (MECBME 2014), Doha, Qatar, February 17-20, 2014.
Sparse signal/image recovery is a challenging topic that has captured a great interest during the last decades, especially in the biomedical field. Many techniques generally try to regularize the considered ill-posed inverse problem by defining appropriate priors for the target signal/image. The target regularization problem can then be solved either in a variational or Bayesian context. However, a little interest has been devoted to the uncertainties about the linear operator, which can drastically alter the reconstruction quality. In this paper, we propose a novel method for signal/image recovery that accounts and corrects the linear operator imprecisions. The proposed approach relies on a Bayesian formulation which is applied to EEG signal recovery. Our results show the promising potential of the proposed method compared to other regularization techniques which do not account for any error affecting the linear operator.
Signal and image processing / Other
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