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共 109 条记录，以下是21-30 订阅

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A new clustering algorithm for PolSAR images segmentation

A new clustering algorithm for PolSAR images segmentation

引用

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

作者： Violeta Roizman Gordana Draskovic Frédéric Pascal L2S/CentraleSupélec Universidad de Buenos Aires Argentina France L2S / CentraleSupélec University Paris-Saclay France

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

This paper deals with polarimetric synthetic aperture radar (PolSAR) image segmentation. More precisely, we present a new robust clustering algorithm designed for non-Gaussian data. The algorithm is based on an expectation-maximization approach. Its novelty is that, in addition to the estimation of each cluster center and covariance matrix, it also provides for each observation an estimation of the scale parameter, allowing a better flexibility when assigning each observation in one cluster. The method performances are evaluated on both simulated and real multi-looked PolSAR data. It is demonstrated that the algorithm outperforms the classical clustering algorithms such as k-means and GMM (Gaussian-based EM algorithm) in various scenarios.

关键词： Clustering algorithms Covariance matrices Image segmentation Robustness Estimation Data models Probability density function

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Maximization of the Fisher Information in PDA

Maximization of the Fisher Information in PDA

引用

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

作者： Jérémy Payan Claude Jauffret Annie-Claude Pérez Aix Marseille Université Université de Toulon CNRS IM2NP CS 60584 83041 Cedex 9 Marseille TOULON France

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

In a cluttered environment, the probabilistic data association (PDA) model allows constructing efficient estimators. In this case, the Fisher information matrix (FIM) is equal to the FIM in the clean environment multiplied by the socalled information reduction factor. This factor depends implicitly on the detection and false alarms probabilities, hence on the threshold prior to the estimation step. The topic of this paper is to seek the optimal threshold, the one that maximizes the information reduction factor. Whereas the Poisson law is used as an approximation in PDA model, here we consider the binomial law for the large false alarms probabilities. An example, coming from signal processing, illustrates our analysis.

关键词： Handheld computers Logic gates Probabilistic logic Target tracking Clutter Signal processing Estimation

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A Comparative Study of the Performance of Parameter Estimation of a 2-D Field Using Line- and Point-Projection sensors

A Comparative Study of the Performance of Parameter Estimati...

引用

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

作者： Shani Gat Hagit Messer School of Electrical Engineering Tel-Aviv University ISRAEL

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

The signal's attenuation measured over wireless Commercial Microwave Links (CMLs) have proven to be an effective tool for opportunistic environmental sensing. By nature, a CML senses the projection along the propagation path of the 2-D field it monitors. Inspired by this application, we present a comparative theoretical study of two families of sensors: in one case the sensors are point samples of the field, and in the other case they are projections along a line of an arbitrary length and of arbitrary orientation, centered at the same locations. The study is done by Cramer-Rao Bounds, used for evaluating the performance of estimating a Gaussian-Shaped field by N sensors in given locations. This study is relevant to IoT applications, where in the first case the sensors are designated, local sensors (e.g., rain gauges) and in the second case the sensors are opportunistic, e.g., CMLs. We present appropriate algorithm-independent theoretical tools to deal with such problems, and relate the characteristics of the monitored field to the potential performance gain when using CMLs.

关键词： Rain Monitoring Tools sensor phenomena and characterization Time measurement Noise measurement

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Spectral Shrinkage of Tyler's $M$-Estimator of Covariance Matrix

Spectral Shrinkage of Tyler's $M$-Estimator of Covariance Ma...

引用

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

作者： Arnaud Breloy Esa Ollila Frédéric Pascal LEME (EA 4416) University Paris Nanterre France Aalto University Finland L2S / CentraleSupelec University Paris-Saclay France

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

Covariance matrices usually exhibit specific spectral structures, such as low-rank ones in the case of factor models. In order to exploit this prior knowledge in a robust estimation process, we propose a new regularized version of Tyler's M-estimator of covariance matrix. This estimator is expressed as the minimizer of a robust M -estimating cost function plus a penalty that is unitary invariant (i.e., that only applies on the eigenvalue) that shrinks the estimated spectrum toward a fixed target. The structure of the estimate is expressed through an interpretable fixed-point equation. A majorization-minimization (MM) algorithm is derived to compute this estimator, and the g-convexity of the objective is also discussed. Several simulation studies illustrate the interest of the approach and also explore a method to automatically choose the target spectrum through an auxiliary estimator.

关键词： Eigenvalues and eigenfunctions Covariance matrices Robustness Mathematical model Estimation Cost function Matrix decomposition

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An Example of Non-Standard Behavior of a Maximum Likelihood Estimator in the Large Sample Regime

An Example of Non-Standard Behavior of a Maximum Likelihood ...

引用

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

作者： Lucien Bacharach Jérôme Galy Éric Chaumette François Vincent Alexandre Renaux Mohammed Nabil El Korso UniversitéParis-Sud/SATIE Cachan France University of Montpellier/LIRMM Montpellier France University of Toulouse/ISAE-Supaéro Toulouse France Université Paris Nanterre/LEME Ville d'Avray France Université Paris-Sud/L2S Gif-sur-Yvette France

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

In this paper, the performance of a maximum likelihood estimator (MLE) for a signal model accounting for possible coherence of the signal sources is studied. It is done by combining a dynamical evolution model of the source amplitude, namely a Gaussian random walk, with the usual observation model, assumed to be Gaussian. This implies an underlying relation between the signal source parameters and both the mean and the covariance matrix of the observations, and hence leads to a more general model than those usually used in array processing, such as the conditional signal model or the unconditionalone. The resulting so-called “generalized conditional MLE” can be expressed both in batch form and in recursive form, which is updated since a term was missing in a previous paper. In addition, we derive the Cramér-Rao bounds associated with a model of this type, accounting for the fluctuations of a radar target's backscattering coefficient. Simulation results highlight a non-standard behavior of the estimators: consistent but not efficient for frequencies, efficient but non-consistent for amplitudes.

关键词： Maximum likelihood estimation Radar Backscatter Covariance matrices Radar antennas Trajectory Simulation

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Projection Back onto Filtered Observations for Speech Separation with Distributed Microphone Array

Projection Back onto Filtered Observations for Speech Separa...

引用

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

作者： Shoko Araki Nobutaka Ono Keisuke Kinoshita Marc Delcroix NTT Communication Science Laboratories NTT Corporation 2-4 Hikaridai Seika-cho Soraku-gun Kyoto Japan Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino-shi Tokyo Japan

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

Many speech enhancement approaches generally estimate a source image on a reference microphone. For example, blind source separation approaches including independent vector analysis (IVA) usually determine the scales of the separated signals by using the projection back onto an observed signal at a reference microphone. However, when using distributed microphones, performance strongly depends on the selection of the reference microphone. Therefore, a reference microphone selection strategy or a speech enhancement method that does not depend on the reference microphone is required. Previously, [Araki et al., IWAENC2018, pp. 316-320, 2018] proposed a blind reference microphone selection strategy. In this paper, we propose a new projection back method that does not depend on the reference microphone. Instead of projecting back onto a reference microphone observation, this paper proposes projecting back onto beamformed observed signals. We also show an equivalent system that can easily realize the proposed projection back approach practically. Experiments with real recordings of meeting conversations show that the proposed projection back approach outperforms conventional projection back approaches.

关键词： Microphone arrays Speech enhancement Signal to noise ratio Speech recognition Synchronization Hidden Markov models

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Performance Analysis of a Low-Rank Detector Under Training Data Contamination

Performance Analysis of a Low-Rank Detector Under Training D...

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

作者： P. Vallet G. Ginolhac F. Pascal P. Forster IMS (CNRS Univ. Bordeaux Bordeaux INP) Talence France LISTIC (Univ. Savoie/Mont-Blanc Polytech Annecy) Annecy France L2S (CNRS Univ. Paris-Sud CentraleSuplec) Gif sur Yvette France SATIE (CNRS Univ. Paris-Sud ENS Paris-Saclay) Cachan France

ISBN: (数字)9781728155494

ISBN: (纸本)9781728155500

We consider the problem of detecting a known M-dimensional target signature vector from an observation corrupted by an additive noise with unknown covariance matrix. In that case, standard statistical methods of detection usually assume that N - “target free” observations are available to perform estimation of the noise covariance matrix. However, in several applications, the target signal may contaminate the training data, resulting in a deviation of the expected performance of the detectors. In this paper, we consider the performance analysis of two low-rank detectors under the assumption that Nc elements of the training data are contaminated by the target signal. More precisely, we derive the asymptotic false alarm and detection probabilities in the high dimensional regime in which both the dimension M, the number of training data N and contaminated data Nc converge to infinity at the same rate. Numerical simulations illustrate the fact that, despite the asymptotic nature of the analysis, the results obtained are accurate for reasonable values of M, N and Nc.

关键词： Training data Eigenvalues and eigenfunctions Covariance matrices Clutter Data models Indexes Performance analysis

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Performance of Range-Only TMA 7

Performance of Range-Only TMA

引用

7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Perez, Annie-Claude Jauffret, Claude Pillon, Denis Univ Toulon & Var Aix Marseille Univ CNRS IM2NP Marseille France CS 60584 F-83041 Toulon 9 France Thonon France Toulon France

Range-only target motion analysis (ROTMA) is the topic of this paper: we focus our study on the numerical aspect and performance of the maximum likelihood estimates (MLE) for some scenarios when the noise polluting th... 详细信息

ISBN: (纸本)9781538612514

关键词： Observers Trajectory Maximum likelihood estimation Legged locomotion Noise measurement Observability Conferences

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computational advances in Sparse L1-norm Principal-Component Analysis of multi-Dimensional Data 7

Computational Advances in Sparse L1-norm Principal-Component...

引用

7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Chamadia, Shubham Pados, Dimitris A. SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

ISBN: (纸本)9781538612514

We consider the problem of extracting a sparse L-1-norm principal component from a data matrix X is an element of R-DxN of N observation vectors of dimension D. Recently, an optimal algorithm was presented in the literature for the computation of sparse L-1-norm principal components with complexity O(N-S) where S is the desired sparsity. In this paper, we present an efficient suboptimal algorithm of complexity O (N-2 (N + D)). Extensive numerical studies demonstrate the near-optimal performance of the proposed algorithm and its strong resistance to faulty measurements/outliers in the data matrix.

关键词： Eigen-vectors faulty data feature extraction L-1-norm L-2-norm outlier resistance sparse principal-component analysis

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A General Class of Recursive Minimum Variance Distortionless Response Estimators 7

A General Class of Recursive Minimum Variance Distortionless...

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7th ieee international workshop on computational advances in multi-sensor adaptive processing (CAMSAP)

作者： Galy, Jerome Chaumette, Eric Vincent, Francois Univ Montpellier 2 LIRMM Montpellier France Univ Toulouse ISAE Supaero Toulouse France

ISBN: (纸本)9781538612514

In deterministic parameters estimation, it is common place to design a minimum variance distortionless response estimator (MVDRE) instead of a maximum likelihood estimator to tackle the problem of identifying the components of observations formed from a linear superposition of individual signals to noisy data. When several observations are available and the individual signals are allowed to perform a random walk between observations, one obtains the general class of linear discrete state-space models. This paper introduces a novel recursive formulation of the MVDREs of individual signals compatible with recursive estimation.

关键词： Estimation Lead Radar computational modeling Conferences Covariance matrices Time measurement

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