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检索条件"任意字段=2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2010"

共 1026 条记录，以下是51-60 订阅

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Improved array self calibration with large sensor position errors for closely spaced sources

Improved array self calibration with large sensor position e...

引用

1st ieee sensor array and multichannel signal processing workshop (sam 2000)

作者： Flanagan, BP Bell, KL Mitre Corp Reston VA 20190 USA

ISBN: (纸本)0780363396

Self-calibration algorithms estimate both source directions-of-arrival (DOAs) and perturbed array response vector parameters, such as sensor locations. Calibration errors are usually assumed to be small and a first order approximation to the perturbed array response vector is often used to simplify the estimation procedure. ln this paper, we improve on a previously presented technique that eliminates the small error assumption. The improved technique has better performance for closely spaced sources for both small and large calibration errors.

关键词： array signal processing Calibration Covariance matrix Direction of arrival estimation Matrix decomposition Multiple signal classification Mutual coupling Phased arrays sensor arrays sensor phenomena and characterization

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Multidimensional ESPRIT: A Coupled Canonical Polyadic Decomposition Approach

Multidimensional ESPRIT: A Coupled Canonical Polyadic Decomp...

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8th ieee sensor array and multichannel signal processing workshop (sam)

作者： Sorensen, Mikael De lathauwer, Lieven Katholieke Univ Leuven EE Dept ESAT STADIUS Ctr Dynam Syst Signal Proc & Data Analyt B-3001 Leuven Heverlee Belgium iMinds Med IT Dept B-3001 Leuven Heverlee Belgium KU Leuven Kulak Grp Sci Engn & Technol B-8500 Kortrijk Belgium

ISBN: (纸本)9781479914814

The ESPRIT method is a classical method for one-dimensional harmonic retrieval. During the past two decades it has become apparent that several applications in signal processing correspond to the less studied Multidimensional Harmonic Retrieval (MHR) problem. In order to accommodate this demand, we propose an extension of ESPRIT to MHR based on the coupled canonical polyadic decomposition. This leads to a dedicated uniqueness condition and an algebraic framework for MHR.

关键词： Canonical decomposition MULTIDIMENSIONAL signal processing harmonics retrieval Esapent coupling

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Multiple Source Resolution for True Time Delay Beam-Space processing

Multiple Source Resolution for True Time Delay Beam-Space Pr...

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ieee 7th sensor array and multichannel signal processing workshop (sam)

作者： Wilkins, Nathan Shaw, Arnab USAF Res Lab Munit Directorate Eglin AFB FL 32542 USA Wright State Univ Dept Elect Engn Dayton OH USA

ISBN: (纸本)9781467310710

The authors examine a technique to apply coherent signal subspace processing in the beam-space of a bank of true time delay beamformers. The technique is shown to be capable of resolving closely spaced sources while being inherently wideband in nature, computationally less expensive than many previous wideband coherent integration techniques, and does not rely on a preliminary angle of arrival estimate. The technique is examined for performance with various levels of noise in the system compared to previous methods and the Cramer-Rao theoretical performance bound.

关键词： true time delay Noise levels space processing Angle of arrival broadband coherent integration coherent signal beamformers

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PERFORMANCE ANALYSIS OF ROOT-MUSIC-BASED DIRECTION-OF-ARRIVAL ESTIMATION FOR ARBITRARY NON-UNIFORM arrayS

PERFORMANCE ANALYSIS OF ROOT-MUSIC-BASED DIRECTION-OF-ARRIVA...

引用

ieee sensor array and multichannel signal processing workshop

作者： Ruebsamen, Michael Gershman, Alex B. Tech Univ Darmstadt Commun Syst Grp D-64283 Darmstadt Germany

We present the asymptotic performance analysis of the interpolated root-MUSIC and manifold separation (MS) techniques for direction-of-arrival (DOA) estimation in arbitrary non-uniform sensor arrays. Our analysis take... 详细信息

ISBN: (纸本)9781424422401

关键词： Direction-of-arrival estimation non-uniform sensor arrays root-MUSIC sensor array processing

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Subspace Tracking with Dynamical Models on the Grassmannian 13

Subspace Tracking with Dynamical Models on the Grassmannian

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13rd ieee sensor array and multichannel signal processing workshop (sam)

作者： Saad-Falcon, Alex Ancelin, Brighton Romberg, Justin Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA

ISBN: (纸本)9798350344820;9798350344813

Tracking signals in dynamic environments presents difficulties in both analysis and implementation. In this work, we expand on a class of subspace tracking algorithms which utilize the Grassmann manifold - the set of linear subspaces of a high-dimensional vector space. We design regularized least squares algorithms based on common manifold operations and intuitive dynamical models. We demonstrate the efficacy of the approach for a narrowband beamforming scenario, where the dynamics of multiple signals of interest are captured by motion on the Grassmannian.

关键词： signal processing subspace estimation beamforming array processing grassmann manifold

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One-Bit Radar processing and Estimation with Time-Varying sampling Thresholds 10

One-Bit Radar Processing and Estimation with Time-Varying Sa...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Ameri, Aria Li, Jian Soltanalian, Mojtaba Univ Illinois Chicago IL 60607 USA Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9781538647523

Target parameter estimation from noisy and quantized received signals is of paramount importance in radar applications. In this paper, we propose a novel method to estimate the unknown target parameters via one-bit sampling, where the samples are produced by comparing the received signal with a time-varying threshold. The proposed approach utilizes a weighted least-squares criterion to establish a connection to previous results in radar target estimation and signal processing. Several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

关键词： One-bit quantization parameter estimation radar time-varying thresholds

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Strategies for principal component analysis in wireless sensor networks

Strategies for principal component analysis in wireless sens...

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8th ieee sensor array and multichannel signal processing workshop (sam)

作者： Ghadban, Nisrine Honeine, Paul Francis, Clovis Mourad-Chehade, Farah Farah, Joumana Univ Libanaise Fac Genie Beirut Lebanon Univ Technol Troyes CNRS Inst Charles Delaunay Troyes France Holy Spirit Univ Kaslik Fac Engn Telecommun Dept Beirut Lebanon

ISBN: (纸本)9781479914814

This paper deals with the issue of monitoring physical phenomena using wireless sensor networks. It provides principal component analysis for the time series of sensors' measurements. Without the need to compute the sample covariance matrix, we derive several in-network strategies to estimate the principal axis, including noncooperative and diffusion strategies. The performance of the proposed strategies is illustrated in the issue of monitoring gas diffusion.

关键词： Principal component analysis wireless sensor network adaptive learning distributed processing

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Computationally Efficient Waveform Design in Spectrally Dense Environment 10

Computationally Efficient Waveform Design in Spectrally Dens...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Yli-Niemi, Markus Vorobyov, Sergiy A. Aalto Univ Dept Signal Proc & Acoust POB 15400 FI-00076 Aalto Finland

ISBN: (纸本)9781538647523

The problem of unimodular radar waveform design with similarity constraint in spectrally dense environment is considered. The corresponding optimization problem is non-convex and additionally large-scale. Indeed, the waveform length can be of several thousands and the waveform has to be designed in every coherent processing interval. Since the problem is non-convex, the majorization-minimization (MaMi) method is used for addressing it, and since it is large-scale, the alternating direction method of multipliers (ADMM) is adopted. Thus, we develop a computationally efficient approach for solving the problem by using MaMi with proper design of majorization function together with alternating ADMM with newly proposed computationally efficient proximal projections. We evaluate the computational cost of the proposed algorithm and show its fast convergence unmatched by existing approaches in terms of simulations.

关键词： Radar signal processing algorithms Optimization array signal processing sensor arrays Conferences"

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A Moving Horizon Convex Relaxation for Mobile sensor Network Localization

A Moving Horizon Convex Relaxation for Mobile Sensor Network...

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8th ieee sensor array and multichannel signal processing workshop (sam)

作者： Simonetto, Andrea Leus, Geert Delft Univ Technol Fac EEMCS NL-2826 CD Delft Netherlands

ISBN: (纸本)9781479914814

In mobile sensor network localization problems we seek to estimate the position of the mobile sensor nodes by using a subset of pair-wise range measurements (among the nodes and with mobile anchors). When the sensor nodes are static, convex relaxations have been shown to provide a remarkably accurate approximate solution to this NP-hard estimation problem. In this paper, we propose a novel convex relaxation to tackle the more challenging dynamic case and we develop a moving horizon convex estimator based on a maximum a posteriori (MAP) formulation. The resulting estimator is then compared to standard extended and unscented Kalman filters both with respect to computational complexity and performance with simulated data. The results are promising, yet a more detailed analysis is needed.

关键词： sensor networks Convexity Mobile Ganglia Kalman filters Horizon complexity classes Moving Relaxation

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Distributed Detection and Imaging in MIMO Radar Network Based on Averaging Consensus 10

Distributed Detection and Imaging in MIMO Radar Network Base...

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10th ieee sensor array and multichannel signal processing workshop (sam)

作者： Zabolotsky, Anton A. Mavrychev, Evgeny A. Nizhnii Novgorod State Tech Univ Dept Informat Radio Syst 24 Minin St Nizhnii Novgorod Russia

ISBN: (纸本)9781538647523

In this paper, we consider distributed signal processing in a MIMO radar network. We suppose that transmitting and receiving nodes of MIMO radar are distributed in a large-scale area. In this network the neighboring receiving nodes communicate with each other to exchange data. A fully distributed algorithm for detection and imaging is proposed. This algorithm is based on the averaging consensus approach. The effectiveness of the proposed technique is confirmed by numerical examples. It is compared to the centralized solution in a fusion center, the local solution in an individual receiving node and the proposed distributed algorithm.

关键词： MIMO radar network distributed signal processing averaging consensus algorithm

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