Philippe Forster

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We carry out a performance analysis of two eigenstructure-based direction-of-arrival (DOA) estimation algorithms, using a series expansion of projection operators (or projectors) on the signal and noise subspaces. In the interest of algebraic simplicity, an operator formalism is utilized rather than a more conventional direct use of eigenvectors and(More)
Recently, a new adaptive scheme [Conte (1995), Gini (1997)] has been introduced for covariance structure matrix estimation in the context of adaptive radar detection under non-Gaussian noise. This latter has been modeled by compound-Gaussian noise, which is the product <b>c</b> of the square root of a positive unknown variable tau (deterministic or random)(More)
This paper deals with covariance matrix estimates in impulsive noise environments. Physical models based on compound noise modeling [spherically invariant random vectors (SIRV), compound Gaussian processes] allow to correctly describe reality (e.g., range power variations or clutter transitions areas in radar problems). However, these models depend on(More)
In the field of asymptotic performance characterization of the conditional maximum-likelihood (CML) estimator, asymptotic generally refers to either the number of samples or the signal-to-noise ratio (SNR) value. The first case has been already fully characterized, although the second case has been only partially investigated. Therefore, this correspondence(More)
We have revisited and solved the problem of establishing lower bounds for the estimation of deterministic parameters by means of a constrained optimization problem. We show that these various bounds (Cramer-Rao, Barankin, Battacharyya) can be easily obtained as the result of an optimization by impozing the bias of the estimator. Simulations results are(More)
A wide variety of actual processing requires a detection step, whose main effect is to restrict the set of observations available for parameter estimation. Therefore, as a contribution to the theoretical formulation of the joint detection and estimation problem, we address the derivation of lower bounds for deterministic parameters conditioned by a binary(More)
Minimal bounds on the mean square error (MSE) are generally used in order to predict the best achievable performance of an estimator for a given observation model. In this paper, we are interested in the Bayesian bound of the Weiss-Weinstein family. Among this family, we have Bayesian Cramer-Rao bound, the Bobrovsky-MayerWolf-Zakai bound, the Bayesian(More)
In this paper, we develop an improved tensor MUSIC algorithm adapted to multidimensional data by means of multilinear algebra tools. This approach allows to preserve the multidimensional structure as the signal and the noise subspaces are estimated from the Higher Order Singular Value Decomposition (HOSVD) of the covariance tensor. The proposed algorithm is(More)