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Variance estimation of modal parameters from output-only and input/output subspace-based system identification

JOURNAL OF SOUND AND VIBRATION 2016年 379卷 1-27页

作者： Mellinger, Philippe Doehler, Michael Mevel, Laurent Inria IFSTTAR I4S Campus Beaulieu F-35042 Rennes France

An important step in the operational modal analysis of a structure is to infer on its dynamic behavior through its modal parameters. They can be estimated by various modal identification algorithms that fit a theoretical model to measured data. When output-only data is available, i.e. measured responses of the structure, frequencies, damping ratios and mode shapes can be identified assuming that ambient sources like wind or traffic excite the system sufficiently. When also input data is available, i.e. signals used to excite the structure, input/output identification algorithms are used. The use of input information usually provides better modal estimates in a desired frequency range. While the identification of the modal mass is not considered in this paper, we focus on the estimation of the frequencies, damping ratios and mode shapes, relevant for example for modal analysis during in-flight monitoring of aircrafts. When identifying the modal parameters from noisy measurement data, the information on their uncertainty is most relevant. In this paper, new variance computation schemes for modal parameters are developed for four subspace algorithms, including output-only and input/output methods, as well as data driven and covariance-driven methods. For the input/output methods, the known inputs are considered as realizations of a stochastic process. Based on Monte Carlo validations, the quality of identification, accuracy of variance estimations and sensor noise robustness are discussed. Finally these algorithms are applied on real measured data obtained during vibrations tests of an aircraft. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： (Operational) modal analysis subspace algorithms Variance estimation Aircraft application

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Convergence Rate Analysis of the Majorize-Minimize subspace Algorithm

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IEEE SIGNAL PROCESSING LETTERS 2016年第9期23卷 1284-1288页

作者： Chouzenoux, Emilie Pesquet, Jean-Christophe Univ Paris Est Lab Informat Gaspard Monge CNRS UMR 8049 F-77454 Marne La Vallee 2 France

State-of-the-art methods for solving smooth optimization problems are nonlinear conjugate gradient, low memory BFGS, and majorize-minimize (MM) subspace algorithms. The MM subspace algorithm that has been introduced more recently has shown good practical performance when compared with other methods on various optimization problems arising in signal and image processing. However, to the best of our knowledge, no general result exists concerning the theoretical convergence rate of the MMsubspace algorithm. This paper aims at deriving such convergence rates both for batch and online versions of the algorithm, and in particular, discusses the influence of the choice of the subspace.

关键词： Convergence rate descent methods learning majorization-minimization (MM) memory gradient methods online optimization subspace algorithms

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A Stochastic Majorize-Minimize subspace Algorithm for Online Penalized Least Squares Estimation

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2017年第18期65卷 4770-4783页

作者： Chouzenoux, Emilie Pesquet, Jean-Christophe INRIA Saclay Ctr Visual Comp F-92295 Chatenay Malabry France Univ Paris Est CNRS LIGM UMR 8049 F-77454 Marne La Vallee France Univ Paris Saclay Cent Supelec Ctr Visual Comp F-92295 Chatenay Malabry France

Stochastic approximation techniques play an important role in solving many problems encountered in machine learning or adaptive signal processing. In these contexts, the statistics of the data are often unknown a priori or their direct computation is too intensive, and they have thus to be estimated online from the observed signals. For batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g., a sparsity promoting function), Majorize-Minimize (MM) methods have recently attracted much interest since they are fast, highly flexible, and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the data fidelity term corresponds to a least squares criterion and the cost function is replaced by a sequence of stochastic approximations of it. In this context, we propose an online version of an MM subspace algorithm and we study its convergence by using suitable probabilistic tools. Simulation results illustrate the good practical performance of the proposed algorithm associated with a memory gradient subspace, when applied to both nonadaptive and adaptive filter identification problems.

关键词： Stochastic approximation optimization subspace algorithms memory gradient methods descent methods recursive algorithms majorization-minimization filter identification Newton method sparsity machine learning adaptive filtering

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A subspace signal processing technique for concealed weapons detection

A subspace signal processing technique for concealed weapons...

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32nd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ibrahim, Ahmed S. Liu, K. J. Ray Novak, Dalma Waterhouse, Rod B. Univ Maryland Dept Elect & Comp Engn College Pk MD 20742 USA Pharad LLC Burnie Tas 21061 Australia

ISBN: (纸本)1424407281

Concealed weapons detection is one of the greatest challenges facing national security nowadays. Recently, it has been shown that each weapon can have a unique fingerprint, which is a set of electromagnetic (EM) resonant frequencies determined by its size, shape, and physical composition. Extracting the resonant frequencies of each weapon is one of the major tasks of any detection system. In this paper, we model the reflected signal from each object as a summation of sinusoidal signals, each at certain frequency equal to one of the object's resonant frequencies. Using this model, we propose a detection approach that is based on a modified version of the MUltiple Signal Classification (MUSIC) algorithm. We show by simulations that each object can be represented using a two-dimensional vector, which consists of its two major resonant frequencies.

关键词： MUSIC algorithm natural resonance signal processing subspace algorithms

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On data preprocessing for subspace methods

On data preprocessing for subspace methods

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39th IEEE Conference on Decision and Control

作者： Bauer, D Vienna Tech Univ Inst Econometr Operat Res & Syst Theory A-1040 Vienna Austria

ISBN: (纸本)0780366387

In modern data analysis often the first step is to perform some data preprocessing, e.g. detrending or elimination of periodic components of known period length. This is normally done using least squares regression. Only afterwards black box models are estimated using either pseudo-maximum-likelihood methods, prediction error methods or subspace algorithms. In this paper it is shown, that for subspace methods this is essentially the same as including the corresponding input variables, e.g. a constant or a trend or a periodic component, as additional input variables. Here essentially means, that the estimates only differ through the choice of initial values.

关键词： subspace algorithms linear systems estimation identification

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Comparisons of subspace identification methods for systems operating on closed-loop

Comparisons of subspace identification methods for systems o...

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作者： Lin, Weilu Qin, S. Joe Ljung, Lennart Department of Chemical Engineering University of Texas at Austin Austin TX 78712 United States Department of Electrical Engineering Linköping University SE-581 83 Linköping Sweden

ISBN: (纸本)008045108X

in this paper, we analyze two recently proposed closed-loop subspace identification methods, referred to as innovation estimation method and whitening filter approach respectively. The similarity and difference between them are investigated in detail. It turns out that all closed-loop subspace identification methods can be classified as one-step, two-step, or multi-stage projection methods. A SISO closed-loop simulation shows that to identify a consistent model the whitening filter approach might require longer future and past horizons than the innovation estimation method. Copyright © 2005 IFAC.

关键词： Closed-loop identification subspace algorithms

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Reduced complexity and optimized face recognition approach based on facial symmetry

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2022年第4期19卷 809-822页

作者： Ahmed, Waqas Bajwa, Usama Ijaz Anwar, Muhammad Waqas Sajid, Muhammad Govt Postgrad Coll Dept Comp Sci Mansehra Pakistan COMSATS Univ Islamabad Dept Comp Sci Lahore Campus Islamabad Pakistan Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur Azad Kashmir Pakistan

Recognition of face images is still a challenging and open research problem. A number of recent algorithms have shown that there is a vast scope in improving recognition accuracy by utilizing facial symmetry for face recognition task. The lower computational complexity and faster processing times make this method well suited for real-time applications. In this paper, we have used only one half of the face image for recognition task against various facial challenges. Keeping in view all the previous related studies that are limited in their scope, an unbiased comparison is presented between full face images and half face images by applying four subspace-based algorithms with four different distance metrics. Experiments are conducted on the two most challenging face databases. The FERET is a benchmark database, which closely simulates real-life scenarios, and LFW which is developed for the problem of unconstrained face recognition.

关键词： subspace algorithms Half face images Face identification Computational complexity

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Spectrum estimation, notch filters, and MUSIC

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2005年第10期53卷 3727-3737页

作者： Mahata, K Univ Newcastle Sch Elect Engn Ctr Complex Dynam Syst & Control Newcastle NSW 2308 Australia

A novel extension of the Multiple Signal Classification (MUSIC) algorithm for the frequency estimation problem is proposed in this work. It is shown that the MUSIC algorithm is a data-dependent optimum notch filter design technique. When the input data are passed through this optimum notch filter, the signal-to-noise ratio (SNR) of the output is minimized. The zeros of the optimum notch filter are then used to estimate the frequencies. In the classical MUSIC algorithm, this optimization is carried out over the set of all finite impulse response (FIR) filters having a fixed order. The framework of this paper allows the use of rational notch filters. The fact that the rational filters have sharper spectral characteristics is used to obtain improved estimates, particularly at low SNR. Some interesting properties of the proposed algorithm are derived, and further perspectives on the implementation aspects are provided. The analytical predictions are substantiated using numerical simulation results.

关键词： frequency estimation MUSIC rational notch filters subspace algorithms

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Signal Processing for Passive Radar Using OFDM Waveforms

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2010年第1期4卷 226-238页

作者： Berger, Christian R. Demissie, Bruno Heckenbach, Joerg Willett, Peter Zhou, Shengli Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA Fraunhofer Inst Commun Informat Proc & Ergon FKIE D-53343 Wachtberg Germany Fraunhofer Inst High Frequency Phys & Radar Tech D-53343 Wachtberg Germany

Passive radar is a concept where illuminators of opportunity are used in a multistatic radar setup. New digital signals, like Digital Audio/Video Broadcast (DAB/DVB), are excellent candidates for this scheme, as they are widely available, can be easily decoded to acquire the noise-free signal, and employ orthogonal frequency division multiplex (OFDM). Multicarrier transmission schemes like OFDM use block channel equalization in the frequency domain, efficiently implemented as a fast Fourier transform, and these channel estimates can directly be used to identify targets based on Fourier analysis across subsequent blocks. In this paper, we derive the exact matched filter formulation for passive radar using OFDM waveforms. We then show that the current approach using Fourier analysis across block channel estimates is equivalent to the matched filter, based on a piecewise constant assumption on the Doppler-induced phase rotation in the time domain. We next present high-resolution algorithms based on the same assumption: first we implement MUSIC as a 2-D spectral estimator using spatial smoothing;then we use the new concept of compressed sensing to identify targets. We compare the new algorithms and the current approach using numerical simulation and experimental data recorded from a DAB network in Germany.

关键词： Compressed sensing multistatic radar MUSIC radar processing sparse estimation subspace algorithms

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Computationally efficient MUSIC based DOA estimation algorithm for FMCW radar

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Journal of Electronic Science and Technology 2023年第1期21卷 46-64页

作者： Bakhtiar Ali Karim Haitham Kareem Ali Department of Communication Engineering Sulaimani Polytechnic UniversitySulaymaniyah70-236Iraq

This paper proposes low-cost yet high-accuracy direction of arrival(DOA)estimation for the automotive frequency-modulated continuous-wave(FMcW)*** existing subspace-based DOA estimation algorithms suffer fromeither high computational costs or low *** aim to solve such contradictory relation between complexity and accuracy by using randomizedmatrix ***,we apply an easily-interpretablerandomized low-rank approximation to the covariance matrix(CM)and R∈C^(M×M)throughthresketch maties in the fom of R≈OBQ^(H).Here the approximately compute its *** is,we first approximate matrix Q∈C^(M×z)contains the orthonormal basis for the range of the sketchmatrik C∈C^(M×z)cwe whichis etrated fom R using randomized unifom counsampling and B∈C^(z×z)is a weight-matrix reducing the approximation *** on such approximation,we are able to accelerate the subspacecomputation by the orders of the magnitude without compromising estimation ***,we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the *** validated by the simulation results,the DOA estimation accuracy of the proposed algorithm,eficient multiple signal classification(E-MUSIC)s high,closely tracks standardMUSIC,and outperforms the well-known algorithms with tremendouslyreduced time ***,the devised method can realize high-resolutionreal-time target detection in the emerging multiple input and multiple output(MIMO)automotive radar systems.

关键词： Computational complexity Direction of arrival(DOA)estimation Frequency-modulated continuous-wave(FMCW)radar subspace algorithms

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