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adaptive target altitude estimation using multipath for 2D radar on spherical earth

Adaptive target altitude estimation using multipath for 2D r...

引用

ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Rong Yang Yaakov Bar-Shalom Independent Researcher Blk 603C Punggol Road Singapore 823603 Department of ECE University of Connecticut Storrs CT USA

This paper presents an adaptive target altitude estimation approach based on multipath detections from a 2D airborne radar. An adaptive multiple model iterated least squares (AMM-ILS) estimator is developed to cope with two challengers of this estimation problem: 1) unknown measurement noise variance for the reflected path; 2) inexistence of an analytical measurement function that relates the target parameters (to be estimated) and the radar measurements. A new method is presented for the numerical evaluation of the Jacobian of the measurement function. Simulation results are presented to demonstrate the estimator's performance under various conditions. The results show that the AMM-ILS estimator yields efficient estimates which are close to their Cramer Rao lower bounds (CRLB).

关键词： Adaptation models Estimation Jacobian matrices Earth Airborne radar Sea measurements

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Weiss-Weinstein bound for change-point estimation

Weiss-Weinstein bound for change-point estimation

引用

ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Lucien Bacharach Alexandre Renaux Mohammed Nabil El Korso Eric Chaumette Univcrsite Paris-Sud/L2S France Universite Paris-Nanterre Nanterre ÃŽle-de-France FR Toulouse-Isae/deos France

We compute the Weiss-Weinstein bound in the context of change-point estimation in a multivariate time series whatever the considered distribution of the data as well the prior. Closed-form expressions are then given in the case of Gaussian observations with change of mean and variance and in the case of parameter change in a Poisson distribution. The proposed bound is shown to be tighter than the previous bounds which were originally derived in the deterministic context and provides a better approximation of the maximum a posteriori estimator global mean square error.

关键词： Yttrium Estimation Context Bayes methods Conferences Time series analysis Closed-form solutions

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Helicopter classification via period estimation and time-frequency masks

Helicopter classification via period estimation and time-fre...

引用

ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Rui Zhang Gang Li Carmine Clemente Pramod K. Varshney Department of Electronic Engineering Tsinghua University Beijing China University of Strathclyde EEE Glasgow UK Department of Electrical Engineering and Computer Science Syracuse University Syracuse NY USA

The rotation of blades of a helicopter induces a Doppler modulation around the main Doppler shift, which is commonly called the micro-Doppler signature and can be used for target classification. In this paper, an automatic helicopter classification method is proposed by estimating the period of the micro-Doppler signature and identifying the number of blades via time-frequency masks. The advantages of this method are threefold: (1) it determines the number of blades automatically; (2) it significantly reduces the computational burden compared to the classical model dictionary-based classification methods; (3) it is robust with respect to the inclination of the helicopter. The effectiveness of the proposed approach is validated by using both synthetic and real data.

关键词： Helicopters Blades Time-frequency analysis Correlation coefficient Estimation Doppler effect computational modeling

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Recursive hybrid CRB for Markovian systems with time-variant measurement parameters

Recursive hybrid CRB for Markovian systems with time-variant...

引用

ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Jerome Galy Alexandre Renaux Eric Chaumette Francois Vincent Pascal Larzabal Universite de Montpellier 2/LIRMM Montpellier France Universite Paris-Sud/LSS Gif-sur-Yvette France ISAE-SUPAERO Universite de Toulouse France Universite Paris-Sud/SATIE Cachan France

In statistical signal processing, hybrid parameter estimation refers to the case where the parameters vector to estimate contains both deterministic and random parameters. Lately computationally tractable hybrid Cramér-Rao lower bounds for discrete-time Markovian dynamic systems depending on unknown time invariant deterministic parameters has been released. However in many applications (radar, sonar, telecoms, ...) the unknown deterministic parameters of the measurement model are time variant which prevents from using the aforementioned bounds. It is therefore the aim of this communication to tackle this issue by introducing new computationally tractable hybrid Cramér-Rao lower bounds.

关键词： Time measurement Estimation Conferences Radar applications Sonar applications

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RMT for whitening space correlation and applications to radar detection

RMT for whitening space correlation and applications to rada...

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ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Romain Couillet Maria S. Greco Jean-Philippe Ovarlez Frédéric Pascal L2S/CentraleSupelec-CNRS-Universite Paris-Sud Gif-sur-Yvette France Dipartimento di Ingegneria dell'lnformazione University of Pisa Pisa Italy ONERA SONDRA/CentraleSupelec Palaiseau France

adaptive radar detection and estimation schemes are often based on the independence of the secondary data used for building estimators and detectors. This paper relaxes this constraint and deals with the non-trivial problem of deriving detection and estimation schemes for joint spatial and temporal correlated radar measurements. Latest results from Random Matrix theory, used for large dimensional regime, allows to build a Toeplitz estimate of the spatial covariance matrix while the temporal covariance matrix is then estimated in a conventional way (Sample Covariance Matrix, M-estimates). These two joint estimates of the spatial and temporal covariance matrices leads to build adaptive Radar Detectors, like adaptive Normalized Matched Filter (ANMF). We show that taking care of the spatial covariance matrix may lead to significant performance improvements compared to classical procedures.

关键词： Covariance matrices Correlation Radar Monte Carlo methods Detectors Signal to noise ratio Estimation

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Anomaly detection and estimation in hyperspectral imaging using Random Matrix Theory tools

Anomaly detection and estimation in hyperspectral imaging us...

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ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Eugénie Terreaux Jean-Philippe Ovarlez Frédéric Pascal L2S/CentraleSupelec-CNRS-Universite Paris-Sud Gif-sur-Yvette France ONERA DEMR/TSI Palaiseau France

Anomaly detection aims to detect sources with different spectral characteristics from the background in an hyperspectral image. Classical tools for anomaly detection and estimation are known to have poor performance when they are used on high dimensional hyperspectral image since typically both the number of available sample and their size are large for this kind of imaging. New estimation methods for the number of anomalies, adapted to large dimensional systems, are required. This article points out the limits of classical methods such as Akaike Information Criterion (AIC) or Minimum Description Length (MDL) criteria and it proposes a new estimator based on Random Matrix Theory results better adapted for hyperspectral imaging. Finally, the proposed method is validated on both Monte-Carlo simulations and on experimental data.

关键词： Hyperspectral imaging Covariance matrices Eigenvalues and eigenfunctions Estimation Signal to noise ratio Monte Carlo methods Gaussian noise

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Resolving scaling ambiguities with the ℓ1/ℓ2 norm in a blind deconvolution problem with feedback

Resolving scaling ambiguities with the ℓ1/ℓ2 norm in a bli...

引用

ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Ernie Esser Tim T.Y. Lin Felix J. Herrmann Rongrong Wang The University of British Columbia Faculty of Medicine Vancouver BC CA Department of Earth University of British Columbia Vancouver BC Canada Department of Mathematics University of British Columbia Vancouver BC Canada

Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even with ℓ 1 constraints. However, when paired with an ℓ 1 /ℓ 2 constraint the feedback allows us to resolve the scaling ambiguity under relatively mild assumptions. Inspired by lifting approaches, we propose to split the sparse signal into positive and negative components and apply an ℓ 1 /ℓ 2 constraint to the difference, thereby obtaining a constraint that is easy to implement. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.

关键词： Deconvolution Convolution Kernel Sea measurements Conferences Earth

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Asymptotic detection performance analysis of the robust adaptive Normalized Matched Filter

Asymptotic detection performance analysis of the robust Adap...

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ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Jean-Philippe Ovarlez Frédéric Pascal Arnaud Breloy ONERA DEMR/TSI SONDRA/CentraleSupelec Palaiseau France L2S/CentraleSupelec-CNRS-Universite Paris-Sud Gif-sur-Yvette France SONDRA/CentraleSupelec Gif-sur-Yvette France

This paper presents two different approaches to derive the asymptotic distributions of the robust adaptive Normalized Matched Filter (ANMF) under both H 0 and H 1 hypotheses. More precisely, the ANMF has originally been derived under the assumption of partially homogenous Gaussian noise, i.e. where the variance is different between the observation under test and the set of secondary data. We propose in this work to relax the Gaussian hypothesis: we analyze the ANMF built with robust estimators, namely the M-estimators and the Tyler's estimator, under the Complex Elliptically Symmetric (CES) distributions framework. In this context, we analyse two asymptotic performance characterization of this robust ANMF. The first approach consists in exploiting the asymptotic distribution of the different covariance matrix estimators while the second approach is to directly exploit the asymptotic distribution of the ANMF distribution built with these estimates.

关键词： Covariance matrices Robustness Matched filters Signal to noise ratio Gaussian noise Estimation Conferences

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Compressed Sensing with uncertainty - the Bayesian estimation perspective

Compressed Sensing with uncertainty - the Bayesian estimatio...

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ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Stéphanie Bernhardt Rémy Boyer Sylvie Marcos Pascal Larzabal L2S Universite Paris-Sud (UPS) CNRS CentraleSupelec Gif-Sur-Yvette France SATIE ENS Cachan Cachan France

The Compressed Sensing (CS) framework outperforms the sampling rate limits given by Shannon's theory. This gap is possible since it is assumed that the signal of interest admits a linear decomposition of few vectors in a given sparsifying Basis (Fourier, Wavelet, ...). Unfortunately in realistic operating systems, uncertain knowledge of the CS model is inevitable and must be evaluated. Typically, this uncertainty drastically degrades the estimation performance of sparse-based estimators in the low noise variance regime. In this work, the Off-Grid (OG) and Basis Mismatch (BM) problems are compared in a Bayesian estimation perspective. At first glance, we are tempted to think that these two acronyms stand for the same problem. However, by comparing their Bayesian Cramèr-Rao Bounds (BCRB) for the estimation of a L-sparse amplitude vector based on N measurements, it is shown that the BM problem has a lower BCRB than the OG one in a general context. To go further into the analysis we provide for i.i.d. Gaussian amplitudes and in the low noise variance regime an interesting closed-form expression of a normalized 2-norm criterion of the difference of the two BCRB matrices. Based on the analysis of this closed-form expression, we obtain two conclusions. Firstly, the two uncertainty problems cannot be confused for a non-zero mismatch error variance and with finite N and L. Secondly, the two problems turn to be similar for any mismatch error variance in the large system regime, i.e., for N, L → ∞ with constant aspect ratio N/L → ρ.

关键词： Dictionaries Uncertainty Bayes methods Estimation Context Compressed sensing Reactive power

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A new inversion method for NMR signal processing

A new inversion method for NMR signal processing

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5th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Yarman, C. E. Monzon, L. Reynolds, M. Heaton, N. WesternGeco Houston TX 77060 USA Univ Colorado Boulder CO 80309 USA HFE Schlumberger Sugar Land TX USA Ctr HFE Schlumberger Sugar Land TX USA

ISBN: (纸本)9781467331463;9781467331449

We present a new, semi-analytic inversion method for nuclear magnetic resonance (NMR) log measurements. Our method represents multiwait-time measurements via short sums of exponentials. The resulting sparse T-2 distribution requires fewer T-2 relaxation times than present in linearized inversion methods. The T-1 relaxation times, and corresponding amplitudes are estimated via convex optimization and a semi-analytic algorithm. We obtain an efficient way to represent the NMR data that can be utilized to estimate petrophysical properties and for compression in logging-while-drilling applications.

关键词： Nuclear Magnetic Resonance Inversion Relaxation time signal processing T2

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