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  • 1. Ali, A
    et al.
    Hoagg, J. B
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Bernstein, D. S
    Growing window recursive quadratic optimization with variable regularization2010Conference paper (Refereed)
  • 2.
    Ali, Asad A.
    et al.
    Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA..
    Hoagg, Jesse B.
    Univ Kentucky, Dept Mech Engn, 151 Ralph G Anderson Bldg, Lexington, KY 40507 USA..
    Mossberg, Magnus
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics.
    Bernstein, Dennis S.
    Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA..
    On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm2016In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 5, 715-735 p.Article in journal (Refereed)
    Abstract [en]

    A sliding-window variable-regularization recursive-least-squares algorithm is derived, and its convergence properties, computational complexity, and numerical stability are analyzed. The algorithm operates on a finite data window and allows for time-varying regularization in the weighting and the difference between estimates. Numerical examples are provided to compare the performance of this technique with the least mean squares and affine projection algorithms. Copyright (c) 2015 John Wiley & Sons, Ltd.

  • 3.
    Boudreau, Jonna
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences.
    Mossberg, Magnus
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics.
    Barbier, Christophe
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Chemical Sciences.
    Experiments to Find On-line Measurements of the Structure of the Tissue Paper SurfaceArticle in journal (Refereed)
    Abstract [en]

    The structure of tissue paper has great influence on the quality of the resulting paper produced. One method of measuring the crepe wavelength on-line is sought in order to improve process control as well as to promote greater precision and uniform quality of the end product. In this study, a probe was used to read the surface of the paper whilst the paper travelled at a low speed. Light from a light emitting diode was emitted at a specific angle and collected at the corresponding reflecting angle, from the paper surface.

     

    Focusing the lenses at 45º angle produced results matching closest to the expected wavelength, and such measurements were made on a numerous commercial papers to validate the method. The collected signal contains a lot of information from the surface of the paper and from reflected signals inside the paper. The signal was processed using a mathematical approach to extract the most common wavelengths for each paper. The measured wavelength was found to closely match measurements made with commercial off-line equipment. This new method has a good initial potential to work on-line, however further investigation regarding the effects of high speeds upon the sampling still has to be carried out. 

  • 4. Butt, N. R
    et al.
    Jakobsson,, A
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Computationally efficient online phase-based frequency estimation of a single tone2009Conference paper (Refereed)
  • 5. Fan, H.
    et al.
    Söderström, T.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Carlsson, B.
    Zou, Y.
    Continuous-time AR process parameter estimation from discrete-time data1998Conference paper (Refereed)
  • 6. Fan, H.
    et al.
    Söderström, T.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Carlsson, B.
    Zou, Y.
    Estimation of continuous-time AR process parameters from discrete-time data1999In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 47, no 5, 1232-1244 p.Article in journal (Refereed)
    Abstract [en]

    The problem of estimating continuous-time autoregressive process parameters from discrete-time data is considered. The basic approach used here is based on replacing the derivatives in the model by discrete-time differences, forming a linear regression, and using the least squares method. Such a procedure is simple to apply, computationally flexible and efficient, and may have good numerical properties. It is known, however, that all standard approximations of the highest order derivative, such as repeated use of the delta operator, gives a biased least squares estimate, even as the sampling interval tends to zero. Some of our previous approaches to overcome this problem are reviewed. Then. two new methods, which avoid the shift in our previous results, are presented. One of them, which is termed bias compensation, is computationally very efficient. Finally, the relationship of the above least squares approaches with an instrumental variable method is investigated. Comparative simulation results are also presented

  • 7.
    Hillström, L.
    et al.
    The Ångström Laboratory, Uppsala University.
    Mossberg, Magnus
    The Ångström Laboratory, Uppsala University.
    Lundberg, B.
    The Ångström Laboratory, Uppsala University.
    Identification of complex modulus from measured strains on an axially impacted bar using least squares2000In: Journal of Sound and Vibration, ISSN 0022-460X, E-ISSN 1095-8568, Vol. 230, no 3, 689-707 p.Article in journal (Refereed)
    Abstract [en]

    The complex modulus of a material with linearly viscoelastic behaviour is identified on the basis of strains which are known, from measurements and sometimes from a free end boundary condition, at three or more sections of an axially impacted bar specimen. The aim is to improve existing identification methods based on known strains at three uniformly distributed sections by increasing the number of sections considered and by distributing them non-uniformly. The increased number of sections results in an overdetermined system of equations from which an approximate solution for the complex modulus is determined using the method of least squares. Through the non-uniform distribution of sections, critical conditions with accompanying large errors at certain frequencies are largely eliminated. Experimental tests were carried out at room temperature with two materials, viz., polypropylene and polymethyl methacrylate, five strain gauge configurations and two kinds of impact excitation. Substantial improvement in the quality of the results for complex modulus was obtained.

  • 8.
    Hoagg, Jesse
    et al.
    Dept. of Mech. Eng., Univ. of Kentucky, Lexington, KY, USA.
    Ali, Asad
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Bernstein, D.S
    Sliding window recursive quadratic optimization with variable regularization2011In: American Control Conference (ACC), 2011, San Francisco: IEEE conference proceedings, 2011, 3275-3280 p.Conference paper (Refereed)
    Abstract [en]

    n this paper, we present a sliding-window variable-regularization recursive least squares algorithm. In contrast to standard recursive least squares, the algorithm presented in this paper operates on a finite window of data, where old data are discarded as new data become available. This property can be beneficial for estimating time-varying parameters. Furthermore, standard recursive least squares uses time-invariant regularization. More specifically, the inverse of the initial covariance matrix in standard recursive least squares can be viewed as a regularization term, which weights the difference between the next estimate and the initial estimate. This regularization is fixed for all steps of the recursion. The algorithm derived in this paper allows for time-varying regularization. In particular, the present paper allows for time varying regularization in the weighting as well as what is being weighted. Specifically, the regularization term can weight the difference between the next estimate and a time-varying vector of parameters rather than the initial estimate.

  • 9.
    Irshad, Yasir
    et al.
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    A comparison of estimation concepts applied to networked control systems2012In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012, Piscataway: IEEE Press, 2012, 114-117 p.Conference paper (Refereed)
    Abstract [en]

    A continuous-time description of networked control systems is considered and the parameters are estimated. The discrete-time description is time-varying due to the random time-delays in the wireless links and therefore difficult to work with. Off-line as well as on-line situations are considered for parameter estimation. In the off-line situation, a linear regression is formed and then the parameters are estimated by the least squares method. In the on-line situation, the estimates of the parameters are recursively updated for each time instance. A comparative study of two different parameter estimation approaches is presented. In the first approach, the parameters are estimated by a simple linear regression. In the second approach, transformation of the differentiation operator to another casual and stable linear operator is made in linear regression to estimate the parameters. A numerical study of these approaches is also presented for comparison.

  • 10.
    Irshad, Yasir
    et al.
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Wireless channel modeling based on stochastic differential equations2011Other (Other academic)
  • 11.
    Irshad, Yasir
    et al.
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics. Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, Torsten
    Division of Systems and Control, Department of Information Technology, Uppsala University.
    System identification in a networked environment using second order statistical properties2013In: Automatica, ISSN 0005-1098, Vol. 49, no 2, 652-659 p.Article in journal (Refereed)
    Abstract [en]

    System identification for networked control is considered. Due to the time-delays in the network, it can be difficult to work with a discrete-time model and a continuous-time model is therefore chosen. A covariance function based method that relies on the second order statistical properties of the output signal, where it is assumed that the input signal samples are from a discrete-time white noise sequence, is proposed for estimating the parameters. The method is easy to use since the actual time instants when new input signal levels are applied at the actuator do not have to be known. An analysis of the networked system and the effects of the time-delays is made, and the results of the analysis motivate and support the chosen approach. Numerical studies indicate that the method is robust to randomly distributed time-delays, packet drop-outs, and additive measurement noise.

  • 12. Jakobsson, Andreas
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Multi-channel detection of narcotics and explosives using NQR signals2006Conference paper (Refereed)
  • 13.
    Jakobsson, Andreas
    et al.
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Using Spatial Diversity to Detect Narcotics and Explosives Using NQR Signals2007In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 55, no 9, 4721-4726 p.Article in journal (Refereed)
    Abstract [en]

    Nuclear quadrupole resonance (NQR) offers an unequivocal method of detecting hidden narcotics and explosives. Unfortunately, the practical use of NQR is restricted by the low signal-to-noise ratio (SNR) and means to improve the SNR are vital to enable a rapid, reliable and convenient system. In this correspondence, we develop two multichannel detectors to counter the typically present radio frequency interference. Numerical simulations indicate that the proposed methods offers a significantly improved robustness to uncertainties in the parameters detailing the examined sample.

  • 14. Jakobsson, Andreas
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Rowe, M. D.
    Smith, J. A. S.
    Exploiting temperature dependency in the detection of NQR signals2005Conference paper (Refereed)
  • 15.
    Jakobsson, Andreas
    et al.
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Rowe, M. D.
    Department of Chemistry, King’s College, London.
    Smith, J. A. S.
    Department of Chemistry, King’s College, London.
    Exploiting temperature dependency in the detection of NQR signals2006In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 54, no 5, 1610-1616 p.Article in journal (Refereed)
    Abstract [en]

    Nuclear quadrupole resonance (NQR) offers an unequivocal method of detecting and identifying land mines. Unfortunately, the practical use of NQR is restricted by the low signal-to-noise ratio (SNR), and the means to improve the SNR are vital to enable a rapid, reliable, and convenient system. In this paper, an approximate maximum-likelihood detector (AML) is developed, exploiting the temperature dependency of the NQR frequencies as a way to enhance the SNR. Numerical evaluation using both simulated and real NQR data indicate a significant gain in probability of accurate detection as compared with the current state-of-the-art approach.

  • 16.
    Jakobsson, Andreas
    et al.
    Karlstad University, Division for Engineering Sciences, Physics and Mathematics.
    Mossberg, Magnus
    Karlstad University, Division for Engineering Sciences, Physics and Mathematics.
    Rowe, Michael D.
    Smith, John A. S.
    Frequency-Selective Detection of Nuclear Quadrupole Resonance Signals2005In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 43, no 11, 2659-2665 p.Article in journal (Refereed)
    Abstract [en]

    Nuclear quadrupole resonance (NQR) offers an unequivocal method of detecting and identifying both hidden explosives, such as land mines, and a variety of narcotics. Unfortunately, the practical use of NQR is restricted by a low signal-to-noise ratio (SNR), and means to improve the SNR are vital to enable a rapid, reliable, and convenient system. In this paper, we introduce a frequency-selective approximate maximum-likelihood (FSAML) detector, operating on a subset of the available frequencies, making it robust to the typically present narrow-band interference. The method exploits the inherent temperature dependency of the NQR frequencies as a way to enhance the SNR. Numerical evaluations, using both simulated and real NQR data, indicate a significant gain in probability of accurate detection as compared to a current state-of-the-art approach.

  • 17. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Estimation of fading channels modeled by stochastic differential equations from unevenly sampled data2007Conference paper (Refereed)
  • 18. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    On possibilities for estimating continuous-time ARMA parameters2003Conference paper (Refereed)
  • 19. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, T.
    Estimation of continuous-time stochastic system parameters2008In: Continuous-time model identification from sampled data / [ed] H. Garnier and L. Wang, Springer-Verlag , 2008Chapter in book (Other academic)
  • 20. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, T.
    Identification of continuous-time ARX models from irregularly sampled data2007In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 52, no 3, 417-427 p.Article in journal (Refereed)
  • 21. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, T.
    Identification of continuous-time ARX models from irregularly sampled data2004Report (Other academic)
  • 22. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, T.
    Practical aspects of continuous-time ARMA system identification2004Report (Other academic)
  • 23. Larsson, E. K.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, T.
    The Cramér-Rao bound for estimation of continuous-time ARX parameters from irregularly sampled data2005Conference paper (Refereed)
  • 24.
    Larsson, Erik K.
    et al.
    Division of Systems and Control, Department of Information Technology, Uppsala University, Sweden .
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Söderström, Torsten
    Division of Systems and Control, Department of Information Technology, Uppsala University, Sweden .
    An overview of important practical aspects of continuous-time ARMA system identification2006In: Circuits, systems, and signal processing, ISSN 0278-081X, E-ISSN 1531-5878, Vol. 25, no 1, 17-46 p.Article in journal (Refereed)
    Abstract [en]

    The problem of estimating the parameters in continuous-time autoregressive moving average (ARMA) processes from discrete-time data is considered. Both direct and indirect methods are studied, and similarities and differences are discussed. A general discussion of the inherent difficulties of the problem is given together with a comprehensive study on how the choice of the sampling interval influences the estimation result. A special focus is given to how the Cramer-Rao lower bound depends on the sampling interval.

  • 25. Lequin, O.
    et al.
    Gevers, M.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Bosmans, E.
    Triest, L.
    Iterative Feedback Tuning of PID parameters: Comparison with classical tuning rules2003In: Control Engineering Practice, ISSN 0967-0661, Vol. 11, no 9, 1023-1033 p.Article in journal (Refereed)
  • 26. León de la Barra, B. A.
    et al.
    Jin, L. H.
    Kim, Y.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Identification of First-Order Time-Delay Systems using Two Different Pulse Inputs2008Conference paper (Refereed)
  • 27. León de la Barra, B. A.
    et al.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Identification of under-damped second-order systems using finite duration rectangular pulse inputs2007Conference paper (Refereed)
  • 28. Mahata, K.
    et al.
    Mousavi, S.
    Söderström, T.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Valdek, U.
    Hillström, L.
    On the use of flexural wave propagation experiments for identification of complex modulus2003In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 11, no 6, 863-874 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the nonparametric estimation of the frequency dependent complex modulus of a viscoelastic material. The strains due to flexural wave propagation in a bar specimen are registered at different cross sections. The time domain data is transformed into frequency domain using discrete Fourier transform and a nonlinear least squares algorithm is then employed to estimate the complex modulus at each frequency. Inherent numerical problems due to associated ill-conditioned matrices are treated with special care. An analysis of the quality of the nonlinear least squares estimate is also carried out. The validity of the theoretical results are confirmed by numerical studies and experimental tests

  • 29. Mahata, K.
    et al.
    Söderström, T.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Hillström, L.
    Mousavi, S.
    Identification of complex elastic modulus from flexural wave propagation experiments2002Conference paper (Refereed)
  • 30. Mahata, K.
    et al.
    Söderström, T.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Hillström, L.
    Mousavi, S.
    On the use of flexural wave propagation experiments for identification of complex modulus2001Report (Other academic)
  • 31.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    A statistical inference method for a subset of long-range dependent FARIMA processes2012In: Statistical Signal Processing Workshop (SSP), 2012 IEEE, IEEE conference proceedings, 2012, 456-459 p.Conference paper (Refereed)
    Abstract [en]

    A subset of long-range dependent FARIMA processes is considered. A method for estimating the parameter that describes the long-range dependency of such a process is suggested. The method is based on an asymptotic expression for the covariance function of the process and gives a closed form solution by means of a weighted linear least squares estimate. The variance of the estimate given by themethod is analyzed and, at the same time, the optimal choice of the weighting is expressed. A numerical illustration of the method and the material in the paper is provided

  • 32.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Analysis of a covariance matching method for continuous-time errors-in-variables identification2007Conference paper (Refereed)
  • 33.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Analysis of a covariance matching method for discrete-time errors-in-variables identification2007Conference paper (Refereed)
  • 34.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Analysis of moments based methods for fractional Gaussian noise estimation2012In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 60, no 7, 3823-3827 p.Article in journal (Refereed)
    Abstract [en]

    Fractional Gaussian noise, given as the increment of fractional Brownian motion, is a stationary Gaussian process characterized by the Hurst parameter. In the paper, moments based estimators of the Hurst parameter are presented and analyzed with respect to asymptotic variance

  • 35.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Controller tuning via minimization of time weighted absolute error2004Conference paper (Refereed)
  • 36.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Errors-in-variables identification through covariance matching: Analysis of a colored measurement noise case2008Conference paper (Refereed)
  • 37.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Estimation of continuous-time stochastic signals from sample covariances2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 2, 821-825 p.Article in journal (Refereed)
    Abstract [en]

    The problem of estimating the parameters in stochastic continuous-time signals, represented as continuous-time autoregressive moving average (ARMA) processes, from discrete-time data is considered. The proposed solution is to fit the covariance function of the process, parameterized by the unknown parameters, to sample covariances. It is shown that the method is consistent, and an expression for the approximate covariance matrix of the estimated parameter vector is derived. The derived variances are compared with empirical variances from a Monte Carlo simulation, and with the Cramer-Rao bound. It turns out that the variances are close to the Cramer-Rao bound for certain choices of the sampling interval and the number of covariance elements used in the criterion function.

  • 38.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Estimation of short-term multipath fading channels modeled by stochastic differential equations2006Conference paper (Refereed)
  • 39.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Experimental design for characterisation of viscoelastic materials2002Conference paper (Other (popular science, discussion, etc.))
  • 40.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Forecasting Electric Power Consumption Using Subspace Algorithms2007In: International Journal of Power and Energy Systems, ISSN 1078-3466, Vol. 27, no 4, 393-397 p.Article in journal (Refereed)
    Abstract [en]

    A new approach for electric power consumption forecasting that consists of using subspace identification techniques is presented in the paper. The new and powerful subspace identification techniques are introduced to the members of the power system engineering community who are not familiar with them, and it is shown that they can be an important tool in this area of electrical engineering. The usefulness of the techniques is illustrated on real data, and the identified models give reliable 24-h ahead predictions.

  • 41.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Gaussian process parameter estimation using zero crossing data from wireless sensors2014In: 2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE Signal Processing Society, 2014, 409-413 p.Conference paper (Refereed)
    Abstract [en]

    The parameters in a general Gaussian process, including the parameters in an additive Gaussian noise process, are estimated based on zero crossing data for the total process and arbitrarily filtered versions thereof. A nonlinear weighted least squares estimate is considered and an analysis of the asymptotic covariance matrix of the estimated parameter vector is made. The proposed estimator and the use of zero crossing data are suitable when information of a process is sent from wireless sensors to a node center for further processing due to an efficient use of available bandwidth.

  • 42.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    High-accuracy instrumental variable identification of continuous-time autoregressive parameters from irregularly sampled noisy data2008In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 56, no 8, 4087-4091 p.Article in journal (Refereed)
    Abstract [en]

    A computationally efficient estimator of continuous-time autoregressive (AR) process parameters from irregularly sampled data affected by discrete-time white measurement noise is presented. It is described how an instrumental variable approach can be used for estimating the AR process parameters with high accuracy. Possible estimators of the incremental variance of the driving continuous-time white noise source and of the variance of the discrete-time white measurement noise are also discussed.

  • 43.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Identification of continuous-time ARX models using sample cross-covariances2005Conference paper (Refereed)
  • 44.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Identification of viscoelastic materials and continuous-time stochastic systems2000Doctoral thesis, monograph (Other academic)
  • 45.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    On identification of continuous-time systems using a direct approach1998Licentiate thesis, monograph (Other academic)
  • 46.
    Mossberg, Magnus
    Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics.
    On the use of two sampling strategies for solving an errors-in-variables problem2015In: 2015 EUROPEAN CONTROL CONFERENCE (ECC), IEEE, 2015, 1778-1783 p.Conference paper (Refereed)
    Abstract [en]

    Two sampling strategies are used for solving an errors-in-variables problem where the system as well as the white measurement noises are of a continuous-time nature. The sampling strategies are integrated sampling, and lowpass filtering followed by instantaneous sampling. Covariance relations are derived and systems of equations are formed for the data obtained from the two sampling strategies, and parameter estimators based on these relations and equations are proposed.

  • 47.
    Mossberg, Magnus
    Karlstad University, Division for Engineering Sciences, Physics and Mathematics.
    Optimal control systems: Book review of D. S. Naidu's book2004In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 49, no 1, 155-156 p.Article, book review (Refereed)
  • 48.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Optimal experimental design for identification of viscoelastic materials2004In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 12, no 4, 578-582 p.Article in journal (Refereed)
  • 49.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Parameter estimation in continuous-time stochastic signals using covariance functions2004Conference paper (Refereed)
  • 50.
    Mossberg, Magnus
    Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
    Parameter estimation in wireless channel networks using second order statistics2008Conference paper (Refereed)
12 1 - 50 of 100
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