Papers by Pascal Larzabal
Cornell University - arXiv, Sep 8, 2016
In order to meet the theoretically achievable imaging performance, calibration of modern radio in... more In order to meet the theoretically achievable imaging performance, calibration of modern radio interferometers is a mandatory challenge, especially at low frequencies. In this perspective, we propose a novel parallel iterative multi-wavelength calibration algorithm. The proposed algorithm estimates the apparent directions of the calibration sources, the directional and undirectional complex gains of the array elements and their noise powers, with a reasonable computational complexity. Furthermore, the algorithm takes into account the specific variation of the aforementioned parameter values across wavelength. Realistic numerical simulations reveal that the proposed scheme outperforms the mono-wavelength calibration scheme and approaches the derived constrained Cramér-Rao bound even with the presence of non-calibration sources at unknown directions, in a computationally efficient manner.
Signal Processing
Radio interferometers are phased arrays producing high-resolution images from the covariance matr... more Radio interferometers are phased arrays producing high-resolution images from the covariance matrix of measurements. Calibration of such instruments is necessary and is a critical task. This is how the estimation of instrumental errors is usually done thanks to the knowledge of referenced celestial sources. However, the use of high sensitive antennas in modern radio interferometers (LOFAR, SKA) brings a new challenge in radio astronomy because there are more sensitive to Radio Frequency Interferences (RFI). The presence of RFI during the calibration process generally induces biases in state-of-the-art solutions. The purpose of this paper is to propose an alternative to alleviate the effects of RFI. For that, we first propose a model to take into account the presence of RFI in the data across multiple frequency channels thanks to a low-rank structured noise. We then achieve maximum likelihood estimation of the calibration parameters with a Space Alternating Generalized Expectation-Maximization (SAGE) algorithm for which we derive originally two sets of complete data allowing close form expressions for the updates. Numerical simulations show a significant gain in performance for RFI corrupted data in comparison with some more classical methods.
Publication in the conference proceedings of EUSIPCO, Aalborg, Denmark, 2010
Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013
Publication in the conference proceedings of EUSIPCO, Barcelona, Spain, 2011
Publication in the conference proceedings of EUSIPCO, Glasgow, Scotland, 2009
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006
Publication in the conference proceedings of EUSIPCO, Trieste, Italy, 1996
Publication in the conference proceedings of EUSIPCO, Viena, Austria, 2004
Publication in the conference proceedings of EUSIPCO, Viena, Austria, 2004
Publication in the conference proceedings of EUSIPCO, Poznan, Poland, 2007
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006
Publication in the conference proceedings of EUSIPCO, Viena, Austria, 2004
A multi-carrier OFDM (Orthogonal Frequency Division Multiplexing) system using a Cyclic Prefix fo... more A multi-carrier OFDM (Orthogonal Frequency Division Multiplexing) system using a Cyclic Prefix for preventing inter-block interference is known to be equivalent to a system based on multiple flat fading parallel transmission channels in the frequency domain. In such a system, the information sent on some carriers might be subject to strong attenuations and could be unrecoverable at the receiver. The aim of this thesis is to investigate a transmission scheme known as Linear Precoded OFDM to overcome these drawbacks.
2020 28th European Signal Processing Conference (EUSIPCO), 2021
In the field of array processing, Direction-Of-Arrival (DOA) estimation of close sources in the p... more In the field of array processing, Direction-Of-Arrival (DOA) estimation of close sources in the presence of modeling errors is a challenging problem. Indeed, the degradation of high-resolution methods on such scenario is well known and documented in the literature. This paper proposes an operational sparse L0-regularized method as an alternative. In sparse DOA estimation methods, the determination of the regularization parameter is a critical point, and it is generally empirically tuned. We first provide, in the presence of modeling errors, a theoretical statistical study to estimate the admissible range for this parameter in the presence of two incoming sources. For close sources, we therefore show that the admissible range is shortened. For an operational system, an off-line predetermination of the regularization parameter is required. We show that its selection is closely connected to the resolution limit for a given modeling error. Numerical simulations are presented to demonstrate the efficiency of the proposed implementation and its superiority in comparison with high-resolution methods.
Sampling a finite stream of filtered pulses violates the bandlimited assumption of the Nyquist-Sh... more Sampling a finite stream of filtered pulses violates the bandlimited assumption of the Nyquist-Shannon sampling theory. However, recent low rate sampling schemes have shown that these sparse signals can be sampled with perfect reconstruction at their rate of innovation which is smaller than the Nyquist's rate. To reach this goal in the presence of noise, an estimation procedure is needed to estimate the time-delay and the amplitudes of each pulse. To assess the quality of any estimator, it is standard to use the Cramer-Rao Bound (CRB) which provides a lower bound on the Mean Squared Error (MSE) of any estimator. In this work, analytic expressions of the Cramer-Rao Bound are proposed for an arbitrary number of filtered pulses. Using orthogonality properties on the filtering kernels, an approximate compact expression of the CRB is provided. The choice of kernel is discussed from the point of view of estimation accuracy.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
In sparse DOA estimation methods, the regularization parameter λ is generally empirically tuned. ... more In sparse DOA estimation methods, the regularization parameter λ is generally empirically tuned. In this paper, we provide a statistical method allowing to estimate an admissible interval where λ must be chosen. This work is conducted in the case of an Uniform Circular Array, well known for its θ invariant performances, and vectorized covariance matrix observation. In the recent work [1], it is shown that the equivalence between the ℓ0-constrained problem and the corresponding regularized one is obtained for λ belonging to a given interval. This interval is conditional to an observation. The purpose of this work is to generalize this result for stochastic observations, providing so an interval I of λ valid in all scenarios for an UCA. This interval is not data dependent. Simulation results validate the proposed approach.
2000 10th European Signal Processing Conference, 2000
This paper deals with high resolution bearing estimation in urban radiocommunication scenarii. In... more This paper deals with high resolution bearing estimation in urban radiocommunication scenarii. Indeed, in such environments, scatterers local to the emitter engender diffuse paths that deteriorate the performances of conventional subspace-based algorithms. A deconvolution technique, involving Linear Prediction methods, is designed to characterize so called distributed sources by returning the mean angle and the angular spreading of the signal angular power density. Two ways of implementation are proposed in two extreme cases of diffuse paths correlations. Simulation results show that this proposed method provides satisfaying results compared to the Cramer Rao-Bound and moreover outperform more famous subspace-based algorithms.
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Papers by Pascal Larzabal