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Jean-pierre Delmas - One of the best experts on this subject based on the ideXlab platform.

  • Direction-finding arrays of directional sensors for randomly located sources
    IEEE Transactions on Aerospace and Electronic Systems, 2016
    Co-Authors: Houcem Gazzah, Jean-pierre Delmas, Sérgio M. Jesus
    Abstract:

    The problem of directional sensor placement and orientation is considered when statistical information about the source direction-of-arrival is available. We focus on two-sensor arrays and form a Cramer-Rao-Bound based cost function that depends on the probability distribution of the coplanar source direction. Proper positioning and orientation of the sensors enable the two-sensor array to have an accuracy comparable to that of a 3 or 4 sensor uniform circular array

  • Direction-finding arrays of directional sensors for randomly located sources
    IEEE Transactions on Aerospace and Electronic Systems, 2016
    Co-Authors: Houcem Gazzah, Jean-pierre Delmas, Sergio M. Jesus Larsys
    Abstract:

    The problem of directional sensor placement and orientation is considered when statistical information about the source direction of arrival is available. We focus on two-sensor arrays and form a cost function based on the Cramer–Rao Bound that depends on the probability distribution of the coplanar source direction. Proper positioning and orientation of the sensors enable the two-sensor array to have an accuracy comparable to that of a three- or four-sensor uniform circular array.

  • On the Cramer Rao Bound and maximum likelihood in passive time delay estimation for complex signals
    ICASSP '12 : IEEE International Conference on Acoustics Speech and Signal Processing, 2012
    Co-Authors: Jean-pierre Delmas, Yann Meurisse
    Abstract:

    This paper is devoted to time delay estimation for wide sense stationary complex circular or noncircular Gaussian signals. Using a theorem by Whittle that we have extended to complex data, closed-form expressions of the Cramer Rao Bound (CRB) are given for the time delay alone in presence of nuisance parameters. In particular, we prove that the CRB for the time delay is weakly reduced for noncircular signals w.r.t. circular signals, except for very low signal to noise ratios (SNR), for which the CRB for rectilinear signals is half of the CRB for circular signals. Then, the maximum likelihood (ML) estimate that extends the generalized cross correlation (GCC) estimate is derived.

  • Closed-form expressions of the exact Cramer-Rao, Bound for parameter estimation of BPSK, MSK, or QPSK waveforms
    IEEE Signal Processing Letters, 2008
    Co-Authors: Jean-pierre Delmas
    Abstract:

    This letter addresses the stochastic Cramer–Rao Bound (CRB) pertaining to the joint estimation of the carrier frequency offset, the carrier phase and the noise and signal powers of binary phase- shift keying (BPSK), minimum shift keying (MSK), and quaternary phase-shift keying (QPSK) modulated signals corrupted by additive white circular Gaussian noise. Because the associated models are governed by simple Gaussian mixture distributions, an explicit expression of the Fisher information matrix is given and an explicit expression for the stochastic CRB of these four parameters are deduced. Specialized expressions for low and high SNR are presented as well. Finally, these expressions are related to the modified CRB and our proposed analytical expressions are numerically compared with the approximate expressions previously given in the literature

  • Gaussian Cramer-Rao Bound for direction estimation of non-circular signals in unknown noise fields
    IEEE Transactions on Signal Processing, 2005
    Co-Authors: Habti Abeida, Jean-pierre Delmas
    Abstract:

    This paper focuses on the stochastic Cramer-Rao Bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these Bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models. Stoica and Nehorai, Ottersten et al, Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these Bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models.

Roslaurent - One of the best experts on this subject based on the ideXlab platform.

Laurent Ros - One of the best experts on this subject based on the ideXlab platform.

  • On-Line Hybrid Cramer-Rao Bound for Oversampled Dynamical Phase and Frequency Offset Estimation
    2009
    Co-Authors: Jordi Vilà Valls, Jean-marc Brossier, Laurent Ros
    Abstract:

    This paper deals with the on-line estimation of a dynamical carrier phase and a frequency offset in a digital receiver. We consider a Brownian phase evolution with a linear drift in a Data Aided scenario. The proposed study is relative to the use of an oversampled signal model after matched filtering, leading to a coloured reception noise and a non-stationary power signal. We derive a closed-form expression of the Hybrid Cramer-Rao Bound (HCRB) for this estimation problem. We use a Binary Offset Carrier (BOC) function as shaping pulse. Our numerical results show the potential gain of using the oversampled signal for estimating the dynamical phase and frequency offset, obtaining better performances than using a classical synchronizer.

  • Analytical Analysis of Bayesian Cramer-Rao Bound for Dynamical Rayleigh Channel Complex Gains Estimation in OFDM System
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Hussein Hijazi, Laurent Ros
    Abstract:

    In this paper, we consider the Bayesian Cramer- Rao Bound (BCRB) for the dynamical estimation of multi-path Rayleigh channel complex gains in data-aided (DA) and nondata-aided (NDA) OFDM systems. This Bound is derived in an on-line and off-line scenarios for time-invariant and time-varying complex gains within one OFDM symbol, assuming the availability of prior information. In NDA context, whereas this true BCRB is hard to evaluate, we present a closed-form expression of a BCRB, i.e., the Asymptotic BCRB (ABCRB) or the Modified BCRB (MBCRB). We discuss, based on the theoretical and simulation results, the interest of using somepast and future observations in terms of Doppler spread for the complex gains estimation.

Habti Abeida - One of the best experts on this subject based on the ideXlab platform.

  • Gaussian Cramer-Rao Bound for direction estimation of non-circular signals in unknown noise fields
    IEEE Transactions on Signal Processing, 2005
    Co-Authors: Habti Abeida, Jean-pierre Delmas
    Abstract:

    This paper focuses on the stochastic Cramer-Rao Bound (CRB) on direction of arrival (DOA) estimation accuracy for noncircular Gaussian sources in the general case of an arbitrary unknown Gaussian noise field parameterized by a vector of unknowns. Explicit closed-form expressions of the stochastic CRB for DOA parameters alone are obtained directly from the Slepian-Bangs formula for general noncircular complex Gaussian distributions. As a special case, the CRB under the nonuniform white noise assumption is derived. Our expressions can be viewed as extensions of the well-known results by Stoica and Nehorai, Ottersten et al., Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these Bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models. Stoica and Nehorai, Ottersten et al, Weiss and Friedlander, Pesavento and Gershman, and Gershman et al. Some properties of these CRBs are proved and finally, these Bounds are numerically compared with the conventional CRBs under the circular complex Gaussian distribution for different unknown noise field models.

  • ICASSP (2) - Stochastic Cramer-Rao Bound of DOA estimates for non-circular Gaussian signals
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: Habti Abeida, Jean-pierre Delmas
    Abstract:

    This paper focuses on the stochastic Cramer-Rao Bound (CRB) on direction of arrival (DOA) estimation accuracy for non-circular Gaussian sources. We derive an explicit expression of the CRB for DOA parameters alone in the case of non-circular complex Gaussian sources by two different methods. One of them consists of computing the asymptotic covariance matrix of the maximum likelihood (ML) estimator, and the other is obtained directly from an extended Slepian-Bangs formula. Finally some properties of this CRB are proved.

Hussein Hijazi - One of the best experts on this subject based on the ideXlab platform.

  • Analytical Analysis of Bayesian Cramer-Rao Bound for Dynamical Rayleigh Channel Complex Gains Estimation in OFDM System
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Hussein Hijazi, Laurent Ros
    Abstract:

    In this paper, we consider the Bayesian Cramer- Rao Bound (BCRB) for the dynamical estimation of multi-path Rayleigh channel complex gains in data-aided (DA) and nondata-aided (NDA) OFDM systems. This Bound is derived in an on-line and off-line scenarios for time-invariant and time-varying complex gains within one OFDM symbol, assuming the availability of prior information. In NDA context, whereas this true BCRB is hard to evaluate, we present a closed-form expression of a BCRB, i.e., the Asymptotic BCRB (ABCRB) or the Modified BCRB (MBCRB). We discuss, based on the theoretical and simulation results, the interest of using somepast and future observations in terms of Doppler spread for the complex gains estimation.