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.

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.

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.

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).

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.

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.

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.

Présentatrion des apports des techniques avancées de Traitement du Signal pour les PMEs

Digital Spectral Analysis provides a single source that offers complete coverage of the spectral analysis domain. This self-contained work includes details on advanced topics that are usually presented in scattered sources throughout the literature. The theoretical principles necessary for the understanding of spectral analysis are discussed in the first four chapters: fundamentals, digital signal processing, estimation in spectral analysis, and time-series models. An entire chapter is devoted to the non-parametric methods most widely used in industry. High resolution methods are detailed in a further four chapters: spectral analysis by stationary time series modeling, minimum variance, and subspace-based estimators. Finally, advanced concepts are the core of the last four chapters: spectral analysis of non-stationary random signals, space time adaptive processing: irregularly sampled data processing, particle filtering and tracking of varying sinusoids. Suitable for students, engineers working in industry, and academics at any level, this book provides a rare complete overview of the spectral analysis domain.