... embedded **in** colored **noise** modeled **in** terms of a com- pound-**Gaussian** ...The **covariance** matrices of the primary **and** the secondary data share a common structure while having ...

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... Terms— **Covariance** **matrix**, **estimation**, radar. 1. INTRODUCTION **AND** PROBLEM STATEMENT An ubiquitous task of most radar systems is to detect the presence of a target, **in** a given range cell, ...

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... considered **in** the sequel, the MF amounts to (17) The matched Þlter assumes that both **and** the density gen- erator are known, which is unrealistic but can serve as a ...some **adaptive** **detection** ...

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... Terms—Bayesian **detection**, heterogenous environments, **knowledge**-**aided** processing, maximum a posteriori ...space **and**/or time signature, **in** a cell under test (CUT), **in** the presence ...

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... cretes **in** some range cells, ...example, **in** the case of a for- ward-looking radar, it is known that the clutter is distributed along an ellipse **in** the angle-Doppler plane, **and** that this el- ...

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... INTRODUCTION **AND** PROBLEM STATEMENT I N RADAR applications, the optimal processing scheme for detecting a target buried **in** disturbance (typically clutter **and** thermal **noise**) consists, under the ...

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... areas, **and** by minimizing the number of **detection** tests. It consists **in** computing a SCM with a few training samples **in** the neighborhood of the CUT (so that we limit clutter heterogeneity which ...

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... the **noise** as the sum of the clutter (due to the ground echoes) **and** the thermal **noise** ...resulting **noise** can apart from a SIRV **and** previous results become ...suboptimal. **In** these ...

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... is Hermitian nonnegative such that sup T kΓ T k < ∞. We have here a model for a rank-one signal corrupted with a **Gaussian** spatially white **and** temporally correlated **noise** with stationary temporal ...

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... Sample **Covariance** **Matrix** (SCM) is an optimal estimator of the ...tails **and** asymmetry hardly compatible with the **Gaussian** ...robust **covariance** **estimation** under non-**Gaussian** ...

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... attention, **and** the quasi totality of recent studies followed the lead of [2] **and** considered α t p as deter- ministic ...authors **knowledge**, no references have addressed **detection** of a ...

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... Optronics **and** Signal 10 Avenue Edouard Belin, 31055 Toulouse France Abstract—**Adaptive** **detection** of a Swerling I-II type target **in** **Gaussian** **noise** with unknown **covariance** ...

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... target **in** ground clutter, using a long integration ...time **adaptive** processing (STAP) cannot be ...known **in**- terference subspace **in** the Doppler domain depending on its radial **and** ...

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... **Adaptive** **detection** of distributed targets has been addressed **in** [1] **and** [2]; **noise** is modeled **in** terms of independent, com- plex normal random vectors with a common ...

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... random **matrix** decompositions: distributions, ...spherical **matrix** distributions, **in**: ...shrinkage **estimation** for elliptically symmetric distributions with unknown **covariance** ...

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... Selliah **in** Selliah ( 1964 ). **In** Tsukuma **and** Kubokawa ( 2016 ) extensions of these estimators to the case p < n are ...indeed, **in** order to whiten data, only a triangular system of equations ...

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... learning **and** signal processing applications require an adequate framework to compare statistical ob- jects, starting with probability ...[1] **and** later by Kan- torovich [2] **in** a transport theory ...

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... Introduction **In** this paper, we show that the wild bootstrap **covariance** **matrix** estimator can be calculated directly, without simulation, since it is simply a traditional heteroskedasticity consistent ...

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... reside **in** the signal processing unit of the system without the need of additional ...the **detection** problem as a binary hypothesis test where primary data (namely those containing target returns) are formed ...

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... parameter **estimation** problem from the model (11) is a linear ...nonlinear **and** nonconvex. Experimentally, both algorithms (19) **and** (23)-(24) are well-behaved, **and** their convergence properties ...

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