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Dive into the research topics where Julien Petitjean is active.

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Featured researches published by Julien Petitjean.


european signal processing conference | 2008

Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue

Julien Petitjean; Eric Grivel; William Bobillet; Patrick Roussilhe

In various applications from radar processing to mobile communication systems based on CDMA or OFDM, M-AR multichannel processes are often considered and may be combined with Kalman filtering. However, the estimations of the M-AR parameter matrices and the autocorrelation matrices of the additive noise and the driving process from noisy observations are key problems to be addressed. In this paper, we suggest solving them as an errors-in-variables issue. In that case, the noisy-observation autocorrelation matrix compensated by a specific diagonal block matrix and whose kernel is defined by the M-AR parameter matrices must be positive semi-definite. Hence, the parameter estimation consists in searching every diagonal block matrix that satisfies this property, in reiterating this search for a higher model order and then in extracting the solution that belongs to both sets. A comparative study is then carried out with existing methods including those based on the Extended Kalman Filter (EKF) and the Sigma-Point Kalman Filters (SPKF). It illustrates the relevance and advantages of the proposed approaches.


ieee radar conference | 2013

Analysis of K-distributed sea clutter and thermal noise in high range and Doppler resolution radar data

Camille Sutour; Julien Petitjean; Simon Watts; Jean-Michel Quellec; Stéphane Kemkemian

This paper deals with the distribution of signal received by airborne radar in Doppler domain for maritime surveillance mission. It is composed of thermal noise and sea clutter which its intensity may be described by a compound K-distribution. The data used was recorded in high range and Doppler resolution by monostatic coherent X-band radar above Mediterranean sea and Atlantic ocean. The statistics of the sea clutter plus noise distributions have been estimated by calculating the complementary cumulative density function of real data. The variation of the fitted shape parameter is compared and discussed in a wide range of data under various conditions.


international conference on acoustics, speech, and signal processing | 2009

Recursive errors-in-variables approach for ar parameter estimation from noisy observations. Application to radar sea clutter rejection

Julien Petitjean; Roberto Diversi; Eric Grivel; Patrick Roussilhe

AR modeling is used in a wide range of applications from speech processing to Rayleigh fading channel simulation. When the observations are disturbed by an additive white noise, the standard Least Squares estimation of the AR parameters is biased. Some authors of this paper recently reformulated this problem as an errors-in-variables (EIV) issue and proposed an off-line solution, which outperforms other existing methods. Nevertheless, its computational cost may be high. In this paper, we present a blind recursive EIV method that can be implemented for real-time applications. It has the advantage of converging faster than the noise-compensated LMS based solutions. In addition, unlike EKF or Sigma Point Kalman filter, it does not require a priori knowledge such as the variances of the driving process and the additive noise. The approach is first tested with synthetic data; then, its relevance is illustrated in the field of radar sea clutter rejection.


international radar conference | 2014

Sea-spike analysis in high range and Doppler resolution radar data

Vincent Corretja; Julien Petitjean; Jean-Michel Quellec; Stéphane Kemkemian; Helene Thuilliez; Simon Watts

This paper deals with the analysis of coherent spiky sea clutter data recorded by a high range and Doppler resolution airborne radar for maritime surveillance mission at low grazing angle. A general description of sea-spikes is given: physical explanations, statistical modelling and identification with three different methods. The following properties are then analysed for our data: 1/ temporal characteristics such as radar cross section, range spread, life time and pulse to pulse correlation with frequency agility, 2/ spectral characteristics such as mean frequency and Doppler spread.


international conference on acoustics, speech, and signal processing | 2011

Evolutive method based on a generalized eigenvalue decomposition to estimate time varying autoregressive parameters from noisy observations

Hiroshi Ijima; Julien Petitjean; Eric Grivel

A great deal of interest has been paid to the estimation of time-varying autoregressive (TVAR) parameters. However, when the observations are disturbed by an additive white measurement noise, using standard least squares methods leads to a weight-estimation bias. In this paper, we propose to jointly estimate the TVAR parameters and the measurement-noise variance from noisy observations by means of a generalized eigenvalue decomposition. It extends to the TVAR case an off-line method that was initially proposed for AR parameter estimation from noisy observations. A comparative study is then carried out with existing methods such as the recursive errors-in-variable approach and Kalman based algorithms.


ieee radar conference | 2011

Coloured transmission based on multicarrier phase coded signals in MIMO radar

Vincent Pereira; Eric Grivel; Julien Petitjean

In radar processing, coloured transmission aims at improving the detection of targets that appear as fast as they disappear. It consists in simultaneously transmitting different waveforms using a wide beam. To differentiate the transmitted waveforms, various orthogonal coding schemes such as phase and frequency coding have been considered. In this paper, we suggest using in the MIMO radar case the so-called multicarrier phase coded signals (MCPC) initially introduced in SISO radar processing. This approach has the advantage of having better performances than phase coding based schemes. In addition, it has a lower computational cost than the frequency coding system.


Traitement Du Signal | 2011

STAP fondé sur une modélisation autorégressive (AR) des interférences : estimation des paramètres AR par filtrage de Kalman

Julien Petitjean; Eric Grivel

In the STAP domain, modeling the interferences as an autoregressive (AR) process with the detector called Parametric Adaptive Matched Filter (PAMF), provides an estimation of the clutter-rejection filter with few training data. The main difficulty of this approach is the estimation of the AR matrices by using the training data. Thus, we propose an on-line estimation based on the Kalman filter and its variants. A comparative study is carried out and illustrates the relevance of such approaches with data provided by the DGA.


international conference on acoustics, speech, and signal processing | 2010

Fixed-point based autoregressive parameter estimation for space time adaptive processing

Julien Petitjean; Eric Grivel; Patrick Roussilhe

Space time adaptive processing (STAP) is useful in radar processing to detect a target by filtering the clutter and the additive thermal noise. A derived version based on a multichannel autoregressive (M-AR) model of the clutter has the advantage of reducing the computational cost. Nevertheless, the estimation of the AR matrix parameters is a key issue because the clutter is not Gaussian in real cases. When dealing with an off-line solution, the multichannel least squares method (MLS) can be considered, but the estimation of the disturbance covariance matrix is required. In this paper, we suggest using the so-called fixed point method since it has “matrix- and texture-constant false alarm rate” property (matrix-CFAR and texture-CFAR) and it provides an unbiased and consistent estimate in a non-Gaussian case. A comparative study is then carried out between off-line M-AR based STAP methods and it points out the relevance of the solution we propose.


european signal processing conference | 2010

A recursive errors-in-variables method for tracking time varying autoregressive parameters from noisy observations

Julien Petitjean; Eric Grivel; Roberto Diversi


european signal processing conference | 2009

H ∞ filtering for autoregressive modeling based Space-Time Adaptive Processing

Julien Petitjean; Eric Grivel; Patrick Roussilhe

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Eric Grivel

University of Bordeaux

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Stéphane Kemkemian

Centre national de la recherche scientifique

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