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Conference Paper

Subband Decomposition Using Multichannel AR Spectral Estimation

Authors: Bonacci David, Mailhes Corinne and Castanié Francis

In Proc. IEEE Int. Conf. Acoust., Speech and Signal Processing (ICASSP), Philadelphia, USA, March 18-23, 2005.

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Subband decomposition has been shown to be a useful tool for spectral estimation, in particular when parametric methods have to be considered. Indeed, the loss of observed samples due to decimation can be compensated by the use of a suitable model, if available. This paper studies a subband multichannel autoregressive spectral estimation (SMASE) method. The proposed method decomposes the observed signal through an appropriate filter bank and processes the decimated signals by means of a multichannel autoregressive (AR) model. This model takes advantage of known correlations between different subband signals. This a priori knowledge allows to improve spectral estimation performance. Simulation results illustrate the interest of the proposed methodology for signals with continuous spectra and for sinusoids.

Signal and image processing / Other

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Improving High Resolution Spectral Analysis Methods for Radar Measurements Using Subband Decomposition

Authors: Bonacci David, Mailhes Corinne and Castanié Francis

In Proc. Int. Workshop on Intelligent Transportation (WIT), Hamburg, Germany, March 15-16, 2005.

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This paper addresses the problem of spec-tral analysis on radar measurements using high res-olution methods. These methods have already been shown to yield better results than Fast Fourier Trans-form (FFT) based methods for accuracy on detected frequencies and more particularly for frequency res-olution. In most applications, these performances are closely related to the performances of range and veloc-ity estimation. In the paper, theoretical study shows the interest of subband decomposition for improving per-formances of frequency estimation in the case of the use of High Resolution methods, while it is shown to be inefficient when using FFT-based algorithms. Some elements of computational cost are given, in order to compare fullband and subband processing when using Fast Least Square Autoregressive (AR) algorithm. Fi-nally, experimental results are given, showing the inter-est of subband decomposition within the frame of radar signal processing either for accuracy and resolution on frequency estimation.

Signal and image processing / Localization and navigation

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Comparison and Evaluation of Quality Criteria for Hyperspectral Imagery

Authors: Christophe Emmanuel, Leger Dominique and Mailhes Corinne

In Proc. SPIE Electronic Imaging, San Jose, USA, vol. 5668, pp. 204-213, January 17-20, 2005.

Hyperspectral data appears to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy. Handling the significant size of hyperspectral data presents a challenge for the user community. To enable efficient data compression without losing the potentiality of hyperspectral data, the notion of data quality is crucial for the development of applications. To assess the data quality, quality criteria relevent to end-user applications are required. This paper proposes a method to evaluate quality criteria. The purpose is to provide quality criteria corresponding well to the impact of degradation on end-user applications. Several quality criteria adapted to hyperspectral context are evaluated. Finally, five criteria are selected to give a good representation of the degradation nature and level affecting hyperspectral data.

Signal and image processing / Earth observation

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The impact of High Resolution Spectral Analysis methods on the performance and design of millimetre wave FMCW radars

Authors: Bonacci David, Mailhes Corinne, Chabert Marie and Castanié Francis

In Proc. Int. Radar Conf. (Radar 2004), October 19-21, 2004.

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This paper addresses the problem of joint measures of range and velocity of moving targets using millimetre wave FMCW radar (in the 77 Ghz range) within the field of automotive applications. The proposed solution is to determine range and velocity using spectral estimation of downconverted signals, theoretically composed of multiple sine functions embedded in noise. As a consequence, their accuracy is closely related to the accuracy of frequency estimation. In this paper, High Resolution spectral analysis methods (such as Auto-Regressive or Prony modeling) are shown to strongly impact the technological design constraints of the radars. More precisely, for a given sampling frequency of the downconverted signal, these methods show their ability either to significantly reduce the bandwidth of the linear frequency modulated radar sweeps although keeping constant the frequency resolution, or, for a given technological design, increase the same figure of merit. Moreover, adequate pre-processing of the signal is described, yielding correction of some 'nasty' non-linear effects (VCO, mixers, ...) as well as denoising received signals. Theoretical study of the performances is given and illustrated on simulated and real signals (provided by the RadarNet project of the 5th Framework Program).

Signal and image processing / Localization and navigation

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PhD Thesis

Intérêt du découpage en sous-bandes pour l'analyse spectrale

Author: Bonacci David

Defended in December 2003

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Subband decomposition has been successfully used in several applications in image and signal processing and telecommunication fields. It consists in presenting a signal at the input of a filterbank and then decimating (sampling rate decreasing) the obtained filtered signals. Some authors recently showed that it can be a powerful tool for spectral estimation, either for the classical one (based on the Fourier transform) or spectral estimation based on parametric modellings. The aim of this PhD is double: first, we present the motivations and drawbacks of parametric spectral estimation in the subbands and then we present several original methods able to take advantage of the good properties of subband decomposition while preventing some of its drawbacks. Particularly, some methods able to cancel spectral overlapping induced by sampling rate decreasing will be developped, as well as other methods permitting to increase the perfromances of spectral estimation using the intercorrelation between the sub-series descended from decimation.

Signal and image processing / Other

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PhD Defense Slides

Intérêt du découpage en sous-bandes pour l'analyse spectrale

Author: Bonacci David

Defended in December 2003

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Subband decomposition has been successfully used in several applications in image and signal processing and telecommunication fields. It consists in presenting a signal at the input of a filterbank and then decimating (sampling rate decreasing) the obtained filtered signals. Some authors recently showed that it can be a powerful tool for spectral estimation, either for the classical one (based on the Fourier transform) or spectral estimation based on parametric modellings. The aim of this PhD is double: first, we present the motivations and drawbacks of parametric spectral estimation in the subbands and then we present several original methods able to take advantage of the good properties of subband decomposition while preventing some of its drawbacks. Particularly, some methods able to cancel spectral overlapping induced by sampling rate decreasing will be developped, as well as other methods permitting to increase the perfromances of spectral estimation using the intercorrelation between the sub-series descended from decimation.

Signal and image processing / Other

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Conference Paper

Improving Frequency Resolution for Correlation-Based Frequency Estimation Methods Using Subband Decomposition

Authors: Bonacci David, Mailhes Corinne and Djurić Petar M.

In Proc. Int. Conf. Acoust., Speech and Signal Processing (ICASSP), April 6-10, 2003.

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Subband decomposition has already been shown to increase the performance of spectral estimators, but induced frequency overlapping may be troublesome, bringing edge effects at subband borders. A recent paper (Bonacci, D. et al., EUSIPCO, 2002) proposed a method (SDFW - subband decomposition and frequency warping) allowing subband decomposition to be performed without aliasing. We modify this subband decomposition in order to improve frequency resolution for any correlation based spectral estimator when applied to the subband outputs. Three main improvements are proposed: the subband decomposition is based on comb filters; the SDFW method warping operation is performed using a complex frequency modulation; the autocorrelation is estimated using all sub-series from each subband. Simulation results demonstrate the anticipated performance of the proposed method.

Signal and image processing / Other

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Subband Decomposition and Frequency Warping for Spectral Estimation

Authors: Bonacci David, Michel Patrice and Mailhes Corinne

In Proc. European Signal and Image Processing Conference (EUSIPCO), Toulouse, France, September 3-6, 2002.

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Subband decomposition has already been shown to increase the performances of spectral estimation but induced frequency overlapping may be troublesome, bringing edge effects, when spectral estimation is applied after subband decomposition and decimation. This paper proposes a new spectral estimation procedure based on subband decomposition and frequency warping which reduces the overlapping frequency problem. Simulation results confirm the interest of this new algorithm.

Signal and image processing / Other

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Spectral Estimation Using Subband Decomposition and Frequency Warping

Authors: Bonacci David, Michel Patrice and Mailhes Corinne

In Proc. Int. Conf. Acoust., Speech and Signal Processing (ICASSP), Orlando, Florida, May 13-17, 2002.

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This paper addresses the problem of frequency overlapping which occurs when spectral estimation is applied after subband decomposition. Subband decomposition has already been shown to increase the performances of spectral estimation but induced frequency overlapping may be troublesome. This paper proposes a new spectral estimation procedure based on sub band decomposition and frequency warping which reduces the overlapping frequency problem. Simulations confirm the interest of this new algorithm.

Signal and image processing / Other

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A New Air Traffic Complexity Metric Based on Dynamical System Modelization

Authors: Delahaye Daniel, Paimblanc Philippe, Histon Jonathan M. and Hansman R. John

In Proceedings of the 21st IEEE Digital Avionics Systems Conference, Volume 1, pp 4A2-1 - 4A2-12

This paper presents new concepts to address the air traffic complexity modeling problem. Two new geometrical metrics have been introduced and have been found very useful to capture typical features of traffic complexity. The covariance metric is very adapted to identify disorder in a set of speed vectors and can be applied for en route airspace (en route airspace is the airspace between airports). Similarly, the Koenig metric identifies easily the curl movement organizations and can be applied to areas around airports where air traffic control procedures impose turns on aircraft trajectories.

Signal and image processing / Aeronautical communication systems and Localization and navigation

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Welcome to our new PhD students & post-docs!

On this picture: Quentin, Antoine, Barbara, Oumaima and Amal (from left to right).

PhD positions available at TeSA

PhD subjects available on the CNES site.
On line application before the 31rst of March.

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Activity Report

The TeSA activity report 2015-2016 is available. Feel free to download and enjoy the reading!

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