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Mossberg, Magnus
Publications (10 of 101) Show all publications
Mossberg, M. & Sinn, M. (2017). Cross-correlations of zero crossings in jointly Gaussian and stationary processes with zero means. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5-9 March 2017, New Orleans, LA: . Paper presented at IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Mar 05-09, 2017, New Orleans, LA (pp. 4286-4290). IEEE
Open this publication in new window or tab >>Cross-correlations of zero crossings in jointly Gaussian and stationary processes with zero means
2017 (English)In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5-9 March 2017, New Orleans, LA, IEEE, 2017, p. 4286-4290Conference paper, Published paper (Refereed)
Abstract [en]

Zero crossing data contain information of a process in compact form and is therefore of interest in wireless sensor networks where only reduced amounts of data can be transmitted. When analyzing the properties of certain algorithms using zero crossing data, the cross-covariance between the zero crossing rates of two jointly Gaussian and stationary processes is needed. The evaluation of such a cross-covariance is considered in the paper and an exact numerical expression as well as an asymptotic expression are presented.

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
National Category
Telecommunications Signal Processing Computer Sciences
Identifiers
urn:nbn:se:kau:diva-65956 (URN)10.1109/ICASSP.2017.7952965 (DOI)000414286204090 ()978-1-5090-4117-6 (ISBN)
Conference
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Mar 05-09, 2017, New Orleans, LA
Available from: 2018-01-25 Created: 2018-01-25 Last updated: 2018-07-06Bibliographically approved
Ali, A. A., Hoagg, J. B., Mossberg, M. & Bernstein, D. S. (2016). On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm. International journal of adaptive control and signal processing (Print), 30(5), 715-735
Open this publication in new window or tab >>On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm
2016 (English)In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 5, p. 715-735Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
John Wiley & Sons, 2016
Keywords
variable regularization, sliding-window RLS, digital signal processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kau:diva-42027 (URN)10.1002/acs.2634 (DOI)000373943300003 ()
Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2017-11-30Bibliographically approved
Mossberg, M. (2016). Performance analysis and filter choice for an errors-in-variables method. In: 2016 EUROPEAN CONTROL CONFERENCE (ECC): . Paper presented at European Control Conference (ECC), JUN 29-JUL 01, 2016, Aalborg, DENMARK (pp. 1371-1376). IEEE
Open this publication in new window or tab >>Performance analysis and filter choice for an errors-in-variables method
2016 (English)In: 2016 EUROPEAN CONTROL CONFERENCE (ECC), IEEE, 2016, p. 1371-1376Conference paper, Published paper (Refereed)
Abstract [en]

An errors-in-variables problem where the system as well as the white measurement noises are in continuous-time is considered. Due to the intrinsic nature of the measurement noises, a strategy of lowpass filtering followed by instantaneous sampling is used for obtaining data from the system. A previously developed covariance function based parameter estimation method is first slightly improved. Thereafter, it is analyzed by evaluating the covariance matrix of the estimated parameter vector. Three different expressions for a generic element of an intermediate matrix in the expression for the covariance matrix are derived. One exact but computationally demanding, one approximate valid for small sampling intervals, and one exact for the case when the sampling interval tends to zero. The covariance matrix can be used for studying the influence of some user parameters, including the choice of the lowpass filter, on the quality of the estimated parameter vector.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kau:diva-65090 (URN)10.1109/ECC.2016.7810480 (DOI)000392695300227 ()978-1-5090-2591-6 (ISBN)
Conference
European Control Conference (ECC), JUN 29-JUL 01, 2016, Aalborg, DENMARK
Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2019-11-10Bibliographically approved
Mossberg, M. (2015). On the use of two sampling strategies for solving an errors-in-variables problem. In: 2015 EUROPEAN CONTROL CONFERENCE (ECC): . Paper presented at European Control Conference (ECC), JUL 15-17, 2015, Linz, AUSTRIA (pp. 1778-1783). IEEE
Open this publication in new window or tab >>On the use of two sampling strategies for solving an errors-in-variables problem
2015 (English)In: 2015 EUROPEAN CONTROL CONFERENCE (ECC), IEEE, 2015, p. 1778-1783Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2015
Keywords
Parameter-estimation, identification, noise
National Category
Other Mechanical Engineering
Research subject
Mechanical Engineering
Identifiers
urn:nbn:se:kau:diva-63519 (URN)10.1109/ECC.2015.7330795 (DOI)000380485400279 ()978-3-9524-2693-7 (ISBN)
Conference
European Control Conference (ECC), JUL 15-17, 2015, Linz, AUSTRIA
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2019-09-20Bibliographically approved
Soderstrom, T., Kreiberg, D. & Mossberg, M. (2014). Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems. Automatica, 50(10), 2597-2605
Open this publication in new window or tab >>Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems
2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 10, p. 2597-2605Article in journal (Refereed) Published
Abstract [en]

A covariance matching approach for identifying errors-in-variables systems is analyzed for the general case. The asymptotic covariance matrix of the jointly estimated system parameters, noise variances and auxiliary parameters is derived. An algorithm for how to compute this covariance matrix from given system descriptions is also provided. The results generalize previous known special cases. Using Monte Carlo analysis, we illustrate the proposed algorithm. The results suggest close agreement between the theoretical and empirical accuracy. (C) 2014 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Pergamon Press, 2014
Keywords
System identification, Errors-in-variables models, Linear systems, Covariance functions, Covariance matrix
National Category
Mathematics
Identifiers
urn:nbn:se:kau:diva-41497 (URN)10.1016/j.automatica.2014.08.020 (DOI)000344207300018 ()
Available from: 2016-04-25 Created: 2016-04-11 Last updated: 2017-11-30Bibliographically approved
Mossberg, M. (2014). Gaussian process parameter estimation using zero crossing data from wireless sensors. In: 2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP): . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 04-09, 2014, Florence, ITALY (pp. 409-413). IEEE
Open this publication in new window or tab >>Gaussian process parameter estimation using zero crossing data from wireless sensors
2014 (English)In: 2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE, 2014, p. 409-413Conference paper, Published 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.

Place, publisher, year, edition, pages
IEEE, 2014
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
Gaussian process, estimation, zero crossings, wireless sensors, accuracy analysis
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kau:diva-41573 (URN)000343655300083 ()978-1-4799-2893-4 (ISBN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 04-09, 2014, Florence, ITALY
Available from: 2016-04-25 Created: 2016-04-11 Last updated: 2019-11-10Bibliographically approved
Söderström, T., Irshad, Y., Mossberg, M. & Zheng, W. X. (2013). On the accuracy of a covariance matching method for continuous-time errors-in-variables identification. Automatica, 49(10), 2982-2993
Open this publication in new window or tab >>On the accuracy of a covariance matching method for continuous-time errors-in-variables identification
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 10, p. 2982-2993Article in journal (Refereed) Published
National Category
Engineering and Technology
Research subject
Energy Technology
Identifiers
urn:nbn:se:kau:diva-29861 (URN)10.1016/j.automatica.2013.07.010 (DOI)000324447500005 ()
Available from: 2013-10-21 Created: 2013-10-21 Last updated: 2017-08-15Bibliographically approved
Irshad, Y., Mossberg, M. & Söderström, T. (2013). System identification in a networked environment using second order statistical properties. Automatica, 49(2), 652-659
Open this publication in new window or tab >>System identification in a networked environment using second order statistical properties
2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 2, p. 652-659Article in journal (Refereed) Published
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.

Keywords
Networked control systems;
National Category
Engineering and Technology
Identifiers
urn:nbn:se:kau:diva-26431 (URN)10.1016/j.automatica.2012.11.039 (DOI)000315003100040 ()
Available from: 2013-02-21 Created: 2013-02-21 Last updated: 2017-12-06Bibliographically approved
Irshad, Y. & Mossberg, M. (2012). A comparison of estimation concepts applied to networked control systems. In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012: . Paper presented at 19th Int. Conf. on Systems, Signals and Image Processing, 11-13 April 2012, Vienna (pp. 114-117). Piscataway: IEEE Press
Open this publication in new window or tab >>A comparison of estimation concepts applied to networked control systems
2012 (English)In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP), 2012, Piscataway: IEEE Press, 2012, p. 114-117Conference paper, Published 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.

Place, publisher, year, edition, pages
Piscataway: IEEE Press, 2012
Series
Systems, Signals and Image Processing, ISSN 2157-8672 ; 2012
Keywords
Actuators, Estimation, Least squares approximation, Linear regression, Networked control systems, Vectors, Wireless communication
National Category
Control Engineering Signal Processing
Identifiers
urn:nbn:se:kau:diva-16065 (URN)978-1-4577-2191-5 (ISBN)
Conference
19th Int. Conf. on Systems, Signals and Image Processing, 11-13 April 2012, Vienna
Available from: 2012-12-03 Created: 2012-12-03 Last updated: 2018-07-20Bibliographically approved
Mossberg, M. & Mossberg, E. (2012). A Note on Parameter Estimation in Lamperti Transformed Fractional Ornstein-Uhlenbeck Processes. In: Kinnaert, Michel (Ed.), IFAC Proceedings Volumes: . Paper presented at 16th IFAC Symp. on System Identification, Brussels Meeting Centre, Brussels, Belgium, July 11-13, 2012 (pp. 1067-1072). Elsevier, 45/16
Open this publication in new window or tab >>A Note on Parameter Estimation in Lamperti Transformed Fractional Ornstein-Uhlenbeck Processes
2012 (English)In: IFAC Proceedings Volumes / [ed] Kinnaert, Michel, Elsevier, 2012, Vol. 45/16, p. 1067-1072Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Elsevier, 2012
Series
System Identification
National Category
Signal Processing Control Engineering Probability Theory and Statistics
Identifiers
urn:nbn:se:kau:diva-14417 (URN)10.3182/20120711-3-BE-2027.00202 (DOI)
Conference
16th IFAC Symp. on System Identification, Brussels Meeting Centre, Brussels, Belgium, July 11-13, 2012
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Available from: 2012-07-31 Created: 2012-07-31 Last updated: 2018-07-16Bibliographically approved
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