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  • 1. Ali Khan, N.
    et al.
    Ali, S.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Direction of arrival estimation using adaptive directional time-frequency distributions2018In: Multidimensional systems and signal processing, ISSN 0923-6082, E-ISSN 1573-0824, Vol. 29, no 2, p. 503-521Article in journal (Refereed)
    Abstract [en]

    Time-frequency distributions (TFDs) allow direction of arrival (DOA) estimation algorithms to be used in scenarios when the total number of sources are more than the number of sensors. The performance of such time-frequency (t-f) based DOA estimation algorithms depends on the resolution of the underlying TFD as a higher resolution TFD leads to better separation of sources in the t-f domain. This paper presents a novel DOA estimation algorithm that uses the adaptive directional t-f distribution (ADTFD) for the analysis of close signal components. The ADTFD optimizes the direction of kernel at each point in the t-f domain to obtain a clear t-f representation, which is then exploited for DOA estimation. Moreover, the proposed methodology can also be applied for DOA estimation of sparse signals. Experimental results indicate that the proposed DOA algorithm based on the ADTFD outperforms other fixed and adaptive kernel based DOA algorithms.

  • 2.
    Ali, Sadiq
    et al.
    Universitat Autònoma de Barcelona (UAB),.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Seco-Granados, Gonzalo
    Universitat Autònoma de Barcelona (UAB),.
    López-Salzedo, José A.
    Universitat Autònoma de Barcelona (UAB),.
    Novel collaborative spectrum sensing based on spatial covariance structure2013In: 2013 Proceedings of the 21st European Signal Processing Conference (EUSIPCO), 2013, p. 6811678-Conference paper (Refereed)
    Abstract [en]

    In collaborative spectrum sensing, spatial correlation in the measurements obtained by sensors can be exploited by adopting Generalized Likelihood Ratio Test (GLRT). In this process the GLRT provides a test statistics that is normally based on the sample covariance matrix of the received signal samples. Unfortunately, problems arise when the dimensions of this matrix become excessively large, as it happens in the so-called large-scale wireless sensor networks. In these circumstances, a huge amount of samples are needed in order to avoid the ill-conditioning of the GLRT, which degenerates when the dimensionality of data is equal to the sample size or larger. To circumvent this problem, we modify the traditional GLRT detector by decomposing the large spatial covariance matrix into small covariance matrices by using properties of the Kronecker Product. The proposed detection scheme is robust in the case of high dimensionality and small sample size. Numerical results are drawn, which show that the proposed detection schemes indeed outperform the traditional approaches when the dimension of data is larger than the sample size.

  • 3. Ali, Sadiq
    et al.
    Ramirez, David
    Jansson, Magnus
    Seco-Granados, Gonzalo
    Lopez-Salcedo, Jose A.
    Multi-antenna spectrum sensing by exploiting spatio-temporal correlation2014In: EURASIP Journal on Advances in Signal Processing, ISSN 1687-6172, E-ISSN 1687-6180, p. 160-Article in journal (Refereed)
    Abstract [en]

    In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this problem, several extensions are proposed that bring robustness to the GLRT in the case of high dimensionality and small sample size. In order to achieve these sample-efficient detection schemes, we modify the GLRT-based detector by exploiting the covariance structure and factoring the large spatio-temporal covariance matrix into spatial and temporal covariance matrices. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.

  • 4.
    Barceló, Guillem Casas
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Panahandeh, Ghazaleh
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Image-Based Floor Segmentation in Visual Inertial Navigation2013In: 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), New York: IEEE , 2013, , p. 6p. 1402-1407Conference paper (Refereed)
    Abstract [en]

    This paper presents a floor segmentation algorithmfor indoor sequences that works with single grey-scale images.The portion of the floor closest to the camera is segmentedby judiciously joining a set of horizontal and vertical lines,previously detected. Since the proposed method is not based oncomputing the vanishing point, the system can deal with anykind of indoor scenes and adapts quickly to camera movements.A second contribution is the detection of moving features forpoints within the segmented floor area. Based on the estimatedcamera ego-motion, the ground plane homography is derived.Then, the expected optical flow for the ground points is calculatedand used for rejecting features that belong to moving obstacles.A key point of the designed method is that no restrictions on thecamera motion are imposed for the homography derivation.

  • 5.
    Bauer, Dietmar
    et al.
    Institute for Econometrics, Operations Research and System Theory, TU Wien, Argentinierstr. 8, A-1040 Vienna, Austria.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Analysis of the Asymptotic Properties of the MOESP Type of Subspace Algorithms2000In: Automatica, ISSN 00051098, Vol. 36, no 4, p. 497-509Article in journal (Refereed)
    Abstract [en]

    The MOESP type of subspace algorithms are used for the identification of linear, discrete time, finite-dimensional state-space systems. They are based on the geometric structure of covariance matrices and exploit the properties of the state vector extensively. In this paper the asymptotic properties of the algorithms are examined. The main results include consistency and asymptotic normality for the estimates of the system matrices, under suitable assumptions on the noise sequence, the input process and the underlying true system.

  • 6.
    Björsell, Niclas
    et al.
    University of Gävle.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Medawar, Samer
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Improved estimate of parametric models for analogue to digital converters by using weighted integral nonlinearity data2010In: 17th Symposium IMEKO TC4 - Measurement of Electrical Quantities, 15th International Workshop on ADC Modelling and Testing, and 3rd Symposium IMEKO TC19 - Environmental Measurements, 2010, p. 597-600Conference paper (Refereed)
    Abstract [en]

    Error modelling has played a major role in generating post-corrections of analogue to digital converters (ADC). Benefits by using parametric models for post-correction are that they requires less memory and that they are easier to identify for arbitrary signals. However, the parameters are estimated in two steps; firstly, the integral nonlinearity (INL) is estimated and secondly, the model parameters. In this paper we propose a method to improve the performance in the second step, by utilizing information about the statistical properties of the first step.

  • 7. Falk, J.
    et al.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Estimation of receiver frequency error in a TDOA-based direction-finding system2004In: Conference Record - Asilomar Conference on Signals, Systems and Computers, 2004, Vol. 2, p. 2079-2083Conference paper (Refereed)
    Abstract [en]

    Direction-finding of radio transmitters is considered and in particular correlation-based time-difference-of-arrival (TDOA) estimation between a pair of intercept receivers. In the target application, the received and down-converted signals are corrupted by a frequency error due to a receiver frequency tuning offset which degrades the performance of the TDOA estimation. Traditionally, the cross-ambiguity function (CAP) is used in a 2D scheme for joint TDOA and frequency error estimation. In this paper, sequential ID frequency error and TDOA estimators are introduced and compared to the 2D method. The 2D method attains the CRLB for both the TDOA and frequency error estimates, but have high computational and memory complexity. The proposed frequency error estimator is outperformed by the 2D method. However, the main objective is to estimate the TDOA and the proposed TDOA estimator have a performance similar to that of the 2D method. The main advantage of the proposed method is a reduction in both computational and memory complexity.

  • 8. Falk, J.
    et al.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Multisource time delay estimation subject to receiver frequency errors2003In: CONFERENCE RECORD OF THE THIRTY-SEVENTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, VOLS 1 AND 2 / [ed] Matthews, MB, 2003, p. 1156-1160Conference paper (Refereed)
    Abstract [en]

    An electronic warfare (EW) system with two spatially separated intercept receivers, targeting military communication systems is considered. The EW system estimates the direct ion-of-arrival via a correlation-based time-difference-of-arrival (TDOA) method without any prior knowledge of the signals-of-interest. An important practical consideration is the oscillator frequencies of the two intercept receivers where a difference, or error, degrades the TDOA estimator performance. A measure of this degradation, or loss in power due to the frequency error, is derived and used to describe the robustness against receiver frequency errors. In the multisource scenario, time and frequency overlapping sources can be separated using direction-filtering of the cross-correlation function between the receiver outputs with good result if one signal at a time can be selected by the direction-filter. However, the performance of the considered estimator is degraded in a multisource scenario compared to the single source case.

  • 9.
    Falk, Johan
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Direction Finding for Electronic Warfare Systems Using the Phase of the Cross Spectral Density2002In: RadioVetenskap och Kommunikation (RVK), 2002, p. 264-268Conference paper (Refereed)
    Abstract [en]

    In modern electronic warfare systems there is a need for direction-finding of transmitters using waveforms for military stealth communication. In this paper, a correlation-based method is investigated utilizing the phase of the cross spectral density to estimate the time-difference-of-arrival from a two-channel digital receiver. A least squares method is reviewed, and its performance is investigated by theoretical analysisand by Monte-Carlo simulations. Proper Cramér-Raobounds are derived. It is shown that the method is statistically efficient for flat spectrum signals. The method is found to be a promising method for use against military communication in an electronic warfare direction-finding system.

  • 10.
    Falk, Johan
    et al.
    Department of Electronic Warfare Systems, Swedish Defence Research Agency Linköping, Sweden.
    Händel, Peter
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Effects of frequency and phase error in electronic warfare TDOA direction-finding systems2003In: Military Communications Conference, IEEE conference proceedings, 2003, p. 118-123Conference paper (Refereed)
    Abstract [en]

    Electronic warfare systems for use against military communication sources include direction-finding. The considered direction-finding electronic-warfare system usestwo intercept receivers which is eavesdropping on thetransmitted signal with no knowledge of the waveformused, or its origin. Down-conversion to baseband is required in order to digitize the received signal. This canbe done using a superheterodyne receiver where an oscillator is used to mix the signal-of-interest to baseband.Errors in frequency and phase between the oscillatorsdegrade the performance. Because of this error, the per-formance derived in previous work by the authors willnot apply since the used model no longer is applicable.The extended model presented here considers the oscil-lator errors. The performance using the extended modelis determined numerically and the result is compared tothe Cramer-Rao lower bound for the ideal system usinga typical signal waveform.

  • 11. Gezici, S.
    et al.
    Bayram, S.
    Gholami, Mohammad Reza
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Optimal jammer placement in wireless localization networks2015In: IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC, IEEE conference proceedings, 2015, p. 665-669Conference paper (Refereed)
    Abstract [en]

    The optimal jammer placement problem is proposed for a wireless localization network, where the aim is to degrade the accuracy of locating target nodes as much as possible. In particular, the optimal location of a jammer node is obtained in order to maximize the minimum of the Cramér-Rao lower bounds for a number of target nodes under location related constraints for the jammer node. Theoretical results are derived to specify scenarios in which the jammer node should be located as close to a certain target node as possible, or the optimal location of the jammer node is determined by two or three of the target nodes. In addition, explicit expressions for the optimal location of the jammer node are derived in the presence of two target nodes. Numerical examples are presented to illustrate the theoretical results.

  • 12. Gezici, S.
    et al.
    Gholami, M. R.
    Bayram, S.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jamming of Wireless Localization Systems2016In: IEEE Transactions on Communications, ISSN 0090-6778, E-ISSN 1558-0857, Vol. 64, no 6, p. 2660-2676, article id 7460155Article in journal (Refereed)
    Abstract [en]

    In this paper, the optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramér-Rao lower bound (CRLB) of target nodes, whereas in the second scheme, the power allocation is performed for the aim of maximizing the minimum CRLB of target nodes. Both the schemes are formulated as linear programs, and a closed-form solution is obtained for the first scheme. For the second scheme, under certain conditions, the property of full total power utilization is specified, and a closed-form solution is obtained when the total power is lower than a specific threshold. In addition, it is shown that non-zero power is allocated to at most NT jammer nodes according to the second scheme in the absence of peak power constraints, where NT is the number of target nodes. In the presence of parameter uncertainty, robust versions of the power allocation schemes are proposed. Simulation results are presented to investigate the performance of the proposed schemes and to illustrate the theoretical results.

  • 13. Gezici, Sinan
    et al.
    Gholami, Mohammad Reza
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Bayram, Suat
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Optimal Jamming of Wireless Localization Systems2015In: 2015 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION WORKSHOP, IEEE , 2015, p. 877-882Conference paper (Refereed)
    Abstract [en]

    In this study, optimal jamming of wireless localization systems is investigated. Two optimal power allocation schemes are proposed for jammer nodes in the presence of total and peak power constraints. In the first scheme, power is allocated to jammer nodes in order to maximize the average Cramer-Rao lower hound (CRLB) of target nodes whereas in the second scheme the power allocation is performed for the aim of maximizing the minimum CRLB of target nodes. Both schemes are formulated as linear programs, and a closed-form expression is obtained for the first scheme. Also, the full total power utilization property is specified for the second scheme. Simulation results are presented to investigate perlbrmance of the proposed schemes.

  • 14.
    Gholami, Mohammad Reza
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Dwivedi, Satyam
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Händel, Peter
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Ranging without time stamps exchanging2015In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, IEEE conference proceedings, 2015, p. 3981-3985Conference paper (Refereed)
    Abstract [en]

    We investigate the range estimate between two wireless nodes without time stamps exchanging. Considering practical aspects of oscillator clocks, we propose a new model for ranging in which the measurement errors include the sum of two distributions, namely, uniform and Gaussian. We then derive an approximate maximum likelihood estimator (AMLE), which poses a difficult global optimization problem. To avoid the difficulty in solving the complex AMLE, we propose a simple estimator based on the method of moments. Numerical results show a promising performance for the proposed technique.

  • 15. Gholami, Mohammad Reza
    et al.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Strom, Erik G.
    Sayed, Ali H.
    Diffusion Estimation Over Cooperative Multi-Agent Networks With Missing Data2016In: IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS, ISSN 2373-776X, Vol. 2, no 3, p. 276-289Article in journal (Refereed)
    Abstract [en]

    In many fields, and especially in the medical and social sciences and in recommender systems, data are gathered through clinical studies or targeted surveys. Participants are generally reluctant to respond to all questions in a survey or they may lack information to respond adequately to some questions. The data collected from these studies tend to lead to linear regression models where the regression vectors are only known partially: some of their entries are either missing completely or replaced randomly by noisy values. In this work, assuming missing positions are replaced by noisy values, we examine how a connected network of agents, with each one of them subjected to a stream of data with incomplete regression information, can cooperate with each other through local interactions to estimate the underlying model parameters in the presence of missing data. We explain how to adjust the distributed diffusion strategy through (de)regularization in order to eliminate the bias introduced by the incomplete model. We also propose a technique to recursively estimate the (de)regularization parameter and examine the performance of the resulting strategy. We illustrate the results by considering two applications: one dealing with a mental health survey and the other dealing with a household consumption survey.

  • 16.
    Gholami, Mohammad Reza
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Keskin, M. F.
    Bilkent University, Ankara, Turkey.
    Gezici, Sinan
    Bilkent University, Ankara, Turkey.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. KTH, School of Electrical Engineering (EES), Information Science and Engineering.
    Cooperative Positioning in Wireless Networks2016In: Wiley Encyclopedia of Electrical and Electronics Engineering, John Wiley & Sons, 2016, p. 1-19Chapter in book (Refereed)
    Abstract [en]

    In this article, we study cooperative positioning in wireless networks in which target nodes at unknown locations locally collaborate with each other to find their locations. We review different models available for positioning and categorize the model-based algorithms in two groups: centralized and distributed. We then investigate a lower bound on the variance of unbiased estimators, namely the Cramer–Rao lower bound, which is a common benchmark in the positioning literature. We finally discuss some open problems and research topics in the area of positioning that are worth exploring in future studies.

  • 17.
    Gudmundson, Erik
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wirfält, Petter
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jakobsson, Andreas
    Dept. of Mathematical Statistics, Lund University.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    An esprit-based parameter estimator for spectroscopic data2012In: 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, IEEE conference proceedings, 2012, p. 77-80Conference paper (Refereed)
    Abstract [en]

    The pulse spin-locking sequence is a common excitation sequence for magnetic resonance and nuclear quadrupole resonance signals, with the resulting measurement data being well modeled as a train of exponentially damped sinusoidals. In this paper, we derive an ESPRIT-based estimator for such signals, together with the corresponding Cramer-Rao lower bound. The proposed estimator is computationally efficient and only requires prior knowledge of the number of spectral lines, which is in general available in the considered applications. Numerical simulations indicate that the proposed method is close to statistically efficient, and that it offers an attractive approach for initialization of existing statistically efficient gradient or search based techniques.

  • 18.
    Göransson, Bo
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Spatial and Temporal Frequency Estimation of Uncorrelated Signals Using Subspace Fitting1996In: IEEE Signal Processing Workshop on Statistical Signal and Array Processing, 1996, p. 94-96Conference paper (Refereed)
    Abstract [en]

    We present a novel method for spatial and temporal frequency estimation in the case of uncorrelated sources. By imposing the diagonal structure given in the signal covariance matrix, it is possible to improve the performance of subspace based estimators. The proposed method combines ideas from subspace and covariance matching methods to yield a non-iterative frequency estimation algorithm. In a numerical example we show that the estimator has a lower small sample resolution threshold than root-MUSIC and similar large sample performance.

  • 19. Hyberg, P.
    et al.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Array interpolation and bias reduction2004In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 52, no 10, p. 2711-2720Article in journal (Refereed)
    Abstract [en]

    Interpolation (mapping) of data from a given antenna array onto the output of a virtual array of more suitable configuration is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. Conditions for preserving DOA error variance under such mappings have been derived by several authors. However, in many cases, such as omnidirectional signal surveillance over multiple octaves, systematic mapping errors will dominate over noise effects and cause significant bias in the DOA estimates. To prevent mapping errors from unduly affecting the DOA estimates, this paper uses a geometrical interpretation of a Taylor series expansion of the DOA estimator criterion function to derive an alternative design of the mapping matrix. Verifying simulations show significant bias reduction in the DOA estimates compared with previous designs. The key feature of the proposed design is that it takes into account the orthogonality between the manifold mapping errors and certain gradients of the estimator criterion function. With the new design, mapping of narrowband signals between dissimilar array geometries over wide sectors and large frequency ranges becomes feasible.

  • 20. Hyberg, P
    et al.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Sector array mapping: Transformation matrix design for minimum MSE2002In: THIRTY-SIXTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS, 2002, p. 1288-1292Conference paper (Refereed)
    Abstract [en]

    This paper treats mapping (interpolation) of the output vector from an existing antenna array onto the output vector of an imaginary array when the directions of arrival (DOA) are known only to within a sector. The problem of constructing a mapping matrix, common to the sector, that minimizes DOA Mean Square Error (MSE) across the sector, is analyzed. We derive a general condition on the mapping errors that prevents them from affecting the calculated DOAs. Thereafter we propose a design, algorithm for the transformation matrix that generates mapping errors fulfilling this condition. Simulations show conspicuous. MSE improvements in relevant scenarios.

  • 21.
    Hyberg, Per
    et al.
    Swedish Defence Research Agency (FOI), SE-172 90, Stockholm, Sweden.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Array interpolation and DOA MSE reduction2005In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 53, no 12, p. 4464-4471Article in journal (Refereed)
    Abstract [en]

    Interpolation or mapping of data from a given real array to data from a virtual array of more suitable geometry is well known in array signal processing. This operation allows arrays of any geometry to be used with fast direction-of-arrival (DOA) estimators designed for linear arrays. In an earlier companion paper [21], a first-order condition for zero DOA bias under such mapping was derived and was also used to construct a design algorithm for the mapping matrix that minimized the DOA estimate bias. This bias-minimizing theory is now extended to minimize not only bias, but also to consider finite sample effects due to noise and reduce the DOA mean-square error (MSE). An analytical first-order expression for mapped DOA MSE is derived, and a design algorithm for the transformation matrix that minimizes this MSE is proposed. Generally, DOA MSE is not reduced by minimizing the size of the mapping errors but instead by rotating these errors and the associated noise subspace into optimal directions relative to a certain gradient of the DOA estimator criterion function. The analytical MSE expression and the design algorithm are supported by simulations that show not only conspicuous MSE,improvements in relevant scenarios, but also a more robust preprocessing for low signal-to-noise ratios (SNRs) as compared with the pure bias-minimizing design developed in the previous paper.

  • 22.
    Hyberg, Per
    et al.
    Swedish Defence Research Agency (FOI), Stockholm, Sweden.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Array Mapping: Optimal Transformation Matrix Design2002In: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2002, p. 2905-2908Conference paper (Refereed)
    Abstract [en]

    Mapping of the data output vector from an existing antenna array onto the data vector of an imaginary array of more suitable configuration is well known in array signal processing. By mapping onto an array manifold of lower dimension or uniform structure for example., processing speed can be improved. Conditions for the retaining of DOA error variance under such mapping have been formulated by several authors but the equally important systematic mapping errors, the bias, has been less treated to date. This paper uses a geometrical interpretation of a Taylor expansion of the DOA estimator cost function to derive an alternative design of the mapping matrix that almost completely removes the bias. The key feature of the proposed design is that it takes the orthogonality between the manifold mapping errors and certain gradients of the estimator cost function into account.

  • 23.
    Hyberg, Per
    et al.
    Swedish Defence Research Agency (FOI), SE-172 90, Stockholm, Sweden.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Array Mapping: Reduced Bias Transformation Matrix Design2002In: Nordic Conference on Radio Science and Communications (RVK), 2002, p. 596-600Conference paper (Other academic)
  • 24.
    Hyberg, Per
    et al.
    Swedish Defence Research Agency (FOI), SE-172 90, Stockholm, Sweden.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Sector Array Mapping: Transformation Matrix Design for Minimum MSE2003Conference paper (Refereed)
  • 25.
    Hybergand, Per
    et al.
    Swedish Defence Research Agency (FOI), Stockholm, Sweden.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Error Minimized Array Mapping Applied to Experimental Data2005In: Nordic Conference on Radio Science and Communications (RVK), 2005Conference paper (Other academic)
  • 26.
    Händel, Peter
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Björsell, Niclas
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Model Based Dynamic Characterization of Analog-Digital-Converters at Radio Frequency2007In: 2007 9th International Symposium on Signal Processing and its Applications, Vol. 1-3, 2007, p. 1403-1408Conference paper (Refereed)
    Abstract [en]

    A dynamic characterization of analog-digital converter integral nonlinearity (INL) is considered. When using a plurality of test frequencies in the measurement set-up, the dynamic errors of the converter are characterized. The INL is modeled by low and high code components - LCF and HCF, respectively. The LCF and HCF are parameterized and a least squares method is derived for the estimation of the parameter values from obtained measurements. A closed form solution to the estimation problem is derived and its performance is illustrated by a numerical example. The proposed method is believed to be fruitful in wide-band characterization of analog-digital converters at radio frequency, and thus of importance for the evaluation of modem and future wireless communication systems.

  • 27.
    Händel, Peter
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Björsell, Niclas
    University of Gävle.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Medawar, Samer
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Modeling the Dynamics of Analog-Digital Converters at Radio Frequency2007Conference paper (Refereed)
  • 28.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    A New Subspace Identification Method for Open and Closed Loop Data2005In: IFAC Proceedings Volumes (IFAC-PapersOnline): Volume 16, 2005, 2005, p. 500-505Conference paper (Refereed)
    Abstract [en]

    Abstract: Subspace methods have emerged as useful tools for the identification of lineartime invariant discrete time systems. Most of the methods have been developed for theopen loop case to avoid difficulties with data correlations due to the feedback. This paperextends some recent ideas for developing subspace methods that can perform well on datacollected both in open and closed loop conditions. Here, a method that aims at minimizingthe prediction errors in several approximate steps is proposed. The steps involve usingconstrained least squares estimation on models with different degrees of structure such asblock-toeplitz, and reduced rank matrices. The statistical estimation performance of themethod is shown to be competitive to existing subspace methods in a simulation example.

  • 29.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Asymptotic Variance Analysis of Subspace Identification Methods2000In: IFAC Symp. on System Identification, 2000Conference paper (Refereed)
    Abstract [en]

    The class of subspace algorithms for system identication is an interesting complement to the maximum likelihood or prediction error methods, especially for multivariable systems. The statistical analysis of the subspace methods is dicult since the estimates depend on the data in a rather complicated manner. Previous results include proofs of generic consistency and asymptotic normality of the estimates. However, no explicit transparent expression for the covariance matrix of the limiting distribution has so far been reported because of the aforementioned diculties. The main objective of this paper is to provide a methodology that simplies the asymptotic analysis of subspace based estimation algorithms. The basic idea is illustrated by deriving the asymptotic covariance matrix corresponding to the estimates of the state-space matrices (or the transfer function estimate) of a quite general subspace algorithm.

  • 30.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    On performance analysis of subspace methods in system identification and sensor array processing1995Licentiate thesis, monograph (Other academic)
    Abstract [en]

    This thesis addresses the issue of performance analysis of subspace-based parameter estimation methods in two different applications, namely system identification and sensor array processing.  The objective is to study the quality of the estimated models as the amount of data increases, and to suggest improvements and give user guidelines.

    First, state-space subspace system identification (4SID) methods are formulated in a linear regression framework. This allows us to analyze the problem in a more traditional way. One advantage is that this explains more clearly the effect of the partition of the data in past and future, which is done in  4SID. Also this formulation is useful to relate and compare different proposed approaches to 4SID. The problem of estimating the poles of dynamical systems is considered. In particular, the statistical asymptotic distributions of the parameter estimates of two different 4SID pole estimation methods are studied. The first method is  common in 4SID  and  makes use of the shift invariance structure of the observability matrix, while the  second method is a recently proposed weighted least-squares method. From these results the choice of user-specified parameters is discussed, and it is shown that this choice indeed may not be obvious for the shift invariance method. A simple example is provided to illustrate the problem. However, this problem can be mitigated using more of the system structure. It is also shown that a proposed row weighting matrix in the subspace estimation step does not affect the asymptotic properties of the pole estimates.

    In the second part focusing on sensor array signal processing, parameter estimation from sparse linear arrays is addressed. An algorithm based on the Expectation-Maximization approach is derived. This is an iterative algorithm for solving  maximum likelihood problems. In our application a powerful method  for uniform linear arrays is used in the maximization step. The use of preprocessing of the covariance matrix before applying a direction of arrival estimation algorithm is also considered. In particular, linear preprocessing of covariance data in conjunction with weighted subspace fitting is analyzed and the asymptotic distribution of the parameter estimates is derived. Some possible applications when the preprocessing and the analysis may be useful are also given.

  • 31.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    On subspace methods in system identification and sensor array signal processing1997Doctoral thesis, monograph (Other scientific)
  • 32.
    Jansson, Magnus
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Stabilitetsundersökning vid lösning av Maxwells ekvationer med finita differensmetoder1992Independent thesis Advanced level (professional degree), 12 credits / 18 HE creditsStudent thesis
  • 33.
    Jansson, Magnus
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Subspace Identification and ARX Modeling2003In: IFAC Symp on System Identification, 2003Conference paper (Refereed)
    Abstract [en]

    In this paper we present a new identification method that points at the closerelationship between high order ARX modeling and subspace identification. A high orderARX model is utilized to obtain initial estimates of certain Markov parameters. Theseparameters are then used to restructure the data model used for subspace identification tofacilitate the estimation of the state sequence. Based on the estimated state sequence, thesystem parameters are estimated by linear regression. The method is shown to be competitiveto existing subspace methods by a simulation example. The method can also be used,without modification, on closed loop data in contrast to most previously published subspaceidentification methods

  • 34.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Göransson, Bo
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    A Subspace Method for Direction of Arrival Estimation of Uncorrelated Emitter Signals1999In: IEEE Transactions on Signal Processing, ISSN 1053-587X, Vol. 47, no 4, p. 945-956Article in journal (Refereed)
    Abstract [en]

    A novel eigenstructure-based method for direction estimation is presented. The method assumes that the emitter signals are uncorrelated. Ideas from subspace and covariance matching methods are combined to yield a noniterative estimation algorithm when a uniform linear array is employed. The large sample performance of the estimator is analyzed. It is shown that the asymptotic variance of the direction estimates coincides with the relevant Cramer-Rao lower bound (CRB). A compact expression for the CRB is derived for the ease when it is known that the signals are uncorrelated, and it is lower than the CRB that is usually used in the array processing literature (assuming no particular structure for the signal covariance matrix). The difference between the two CRBs can be large in difficult scenarios. This implies that in such scenarios, the proposed methods has significantly better performance than existing subspace methods such as, for example, WSF, MUSIC, and ESPRIT. Numerical examples are provided to illustrate the obtained results.

  • 35.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Göransson, Bo
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Analysis of a Subspace-based Spatial Frequency Estimator1997In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-97), 1997, p. 4001-4004Conference paper (Refereed)
    Abstract [en]

    In a previous paper we presented a novel method for spatial and temporal frequency estimation assuming that the sources are uncorrelated. The current paper analyzes this method in the case of spatial frequency estimation. In particular an optimal weighting matrix is derived and it is shown that the asymptotic variance of the frequency estimates coincides with the relevant Cramer-Rao lower bound. This means that the estimator is in large samples an efficient subspace-based spatial frequency estimator. The proposed method thus utilizes the a priori knowledge about the signal correlation as opposed to previously known subspace estimators. Moreover, when a uniform linear array is employed, it is possible to implement the estimator in a non-iterative fashion.

  • 36.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ljung, S. P. O.
    Saab-Scania AB, Saab Military Aircraft S-58188 Linköping, SWEDEN.
    Bäckström, M. G.
    Saab-Scania AB, Saab Military Aircraft S-58188 Linköping, SWEDEN.
    Wahlgren, B. I.
    Saab-Scania AB, Saab Military Aircraft S-58188 Linköping, SWEDEN.
    Efficient Implementation of a Submodel for Composite Materials to be Combined with the FDTD-Algorithm1993In: COMPUMAG conference on the computation of electromagnetic fields, 1993Conference paper (Refereed)
  • 37.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ljung, S.P.O
    Saab-Scania AB, Saab Military Aircraft S-58188 Linkoping, SWEDEN.
    Bäckström, M. G.
    Saab-Scania AB, Saab Military Aircraft S-58188 Linkoping, SWEDEN.
    Wahlgren, B. I.
    Saab-Scania AB, Saab Military Aircraft S-58188 Linkoping, SWEDEN.
    Efficient Implementation of a Submodel for Composite Materials to be Combined with the FDTD-Algorithm1994In: IEEE Transactions on Magnetics, ISSN 0018-9464, Vol. 30, no 5, p. 3188-3191Article in journal (Refereed)
    Abstract [en]

    A submodel to be used for thin sheets of semiconducting materials in combination with the finite difference time domain algorithm for solving Maxwell's equations is derived. Emphasis is concentrated on accomplishing an efficient and robust algorithm. Stability properties of the combined model are also investigated

  • 38.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Covariance Preprocessing in Weighted Subspace Fitting1995In: IEEE/IEE workshop on signal processing methods in multipath environments, 1995, p. 23-32Conference paper (Refereed)
  • 39.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Structured Covariance Matrix Estimation: A Parametric Approach2000In: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 2000, p. 3172-3175Conference paper (Refereed)
    Abstract [en]

    The problem of estimating a positive semi-definite Toeplitz covariance matrix consisting of a low rank matrix plus a scaled identity from noisy data arises in many applications. We propose a computationally attractive (noniterative) covariance matrix estimator with certain optimality properties. For example, under suitable assumptions the proposed estimator achieves the Cramer-Rao lower bound on the covariance matrix parameters. The resulting covariance matrix estimate is also guaranteed to possess all of the structural properties of the true covariance matrix. Previous approaches to this problem have either resulted in computationally unattractive iterative solutions or have provided estimates that only satisfy some of the structural relations

  • 40.
    Jansson, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Ottersten, Björn
    KTH, School of Electrical Engineering (EES), Signal Processing.
    Viberg, Mats
    Department of Electrical Engineering, Linkoping University, Linkoping, Sweden..
    Swindlehurst, Andrew Lee
    Brigham Young University, Utah ,US.
    Optimal Subspace Techniques for DOA Estimation in the Presence of Noise and Model Errors2006In: Space-Time Wireless Systems: From Array Processing to MIMO Communications / [ed] Bölcskei, H.; Gesbert, D.; Papadias, C.; Veen, A. J. van der, Cambridge University Press , 2006Chapter in book (Other academic)
  • 41.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Stoica, Petre
    Department of Systems and Control, Uppsala University, Uppsala, Sweden.
    Analysis of Forward-Only and Forward-Backward Sample Covariances1999In: International Conference on Acoustics, Speech, and Signal Processing, 1999, Vol. 5, p. 2825-2828Conference paper (Refereed)
    Abstract [en]

    In some applications the covariance matrix of the observations is not only symmetric with respect to its main diagonal but also with respect to the anti-diagonal. The standard forward-only sample covariance estimate does not impose this extra symmetry. In such cases one often uses the so-called forward-backward sample covariance estimate. In this paper, a direct comparative study of the relative accuracy of the two sample estimates is performed. An explicit expression for the difference between the estimation error covariance matrices of the two sample estimates is given. The presented results are also useful in the analysis of estimators based on either of the two sample covariances. As an example, spatial power estimation by means of the Capon method is considered. It is shown that Capon based on the forward-only sample covariance (F-Capon) underestimates the power spectrum, and also that the bias for Capon based on the forward-backward sample covariance is half that of F-Capon.

  • 42.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Stoica, Petre
    Department of Systems and Control, Uppsala University, Uppsala, Sweden.
    Forward-Only and Forward-Backward Sample Covariances – A Comparative Study1999In: Signal Processing, ISSN 01651684, Vol. 77, no 3, p. 235-245Article in journal (Refereed)
    Abstract [en]

    In some applications the covariance matrix of the observations enjoys a particular symmetry: it is not only symmetric with respect to its main diagonal but also with respect to the anti-diagonal. The standard forward-only sample covariance estimate does not impose this extra symmetry. In such cases one often uses the so-called forward-backward sample covariance estimate. In this paper, a direct comparative study of the relative accuracy of the two sample covariance estimates is performed. An explicit expression for the difference between the estimation error covariance matrices of the two sample covariance estimates is given. This expression shows quantitatively the gain of using the forward-backward estimate compared to the forward-only estimate. The presented results are also useful in the analysis of estimators based on either of the two sample covariances. As an example, spatial power estimation by means of the Capon method is considered. Using a second-order approximation, it is shown that Capon based on the forward-only sample covariance (F-Capon) underestimates the power spectrum, and also that the bias for Capon based on the forward-backward sample covariance is half that of F-Capon.

  • 43.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Stoica, Petre
    Department of Systems and Control, Uppsala University, Uppsala, Sweden.
    On forward -backward mode for direction of arrival estimation1998In: European Signal Processing Conference, 1998, Vol. 3, p. 189-192Conference paper (Refereed)
    Abstract [en]

    We apply the MODE (method of direction estimation) principle to the forward-backward (FB) covariance of the output vector of a sensor array to obtain what we call the FB-MODE procedure. The derivation of FBMODE is an interesting exercise in matrix analysis,the outcome of which was somewhat unexpected: FBMODE simply consists of applying the standard MODEapproach to the eigenelements of the FB sample covariance matrix. By using an asymptotic expansion technique we also establish the surprising result that FB-MODE is outperformed, from a statistical standpoint, by the standard MODE applied to the forward only sample covariance (F-MODE). We believe this tobe an important result that shows that the FB approach,which proved quite useful for improving the performance of many suboptimal array processing methods, shouldnot be used with a statistically optimal method such asF-MODE.

  • 44.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Stoica, Petre
    Uppsala University, Uppsala, Sweden.
    Optimal Yule Walker Method for Pole Estimation of ARMA Signals2003In: IFAC Symp on System Identification, 2003Conference paper (Refereed)
    Abstract [en]

    In this paper we reconsider the analysis and implementation of weighted YuleWalker or instrumental variable methods for estimating the AR parameters of ARMA signals.We present a simplified analysis and propose a new estimate of the optimal weighting matrixleading to more accurate parameter estimates compared to previous approaches.

  • 45.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Swindlehurst, Andrew Lee
    Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602 USA.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Robust Weighted Subspace Fitting in the Presence of Array Model Errors1998In: Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE , 1998, p. 1961-1964Conference paper (Refereed)
    Abstract [en]

    Model error sensitivity is an issue common to all high resolution direction of arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to array calibration errors have also appeared in the literature. Herein, a weighted subspace fitting method for a wide class of array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly enough, the method reduces to the WSF (MODE) estimator if no model errors are present. On the other hand, when model errors dominate, the proposed method turns out to be equivalent to the “model-errors-only subspace fitting method”. Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated.

  • 46.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Swindlehurst, Andrew Lee
    Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602 USA.
    Ottersten, Björn
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Weighted subspace fitting for general array error models1998In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 46, no 9, p. 2484-2498Article in journal (Refereed)
    Abstract [en]

    Model error sensitivity is an issue common to all high-resolution direction-of-arrival estimators. Much attention has been directed to the design of algorithms for minimum variance estimation taking only finite sample errors into account. Approaches to reduce the sensitivity due to army calibration errors have also appeared in the literature. Herein, one such approach is adopted that assumes that the errors due to finite samples and model errors are of comparable size. A weighted subspace fitting method for very general array perturbation models is derived. This method provides minimum variance estimates under the assumption that the prior distribution of the perturbation model is known. Interestingly, the method reduces to the WSF (MODE) estimator if no model errors are present, Vice versa, assuming that model errors dominate, the method specializes to the corresponding "model-errors-only subspace fitting method." Unlike previous techniques for model errors, the estimator can be implemented using a two-step procedure if the nominal array is uniform and linear, and it is also consistent even if the signals are fully correlated. The paper also contains a large sample analysis of one of the alternative methods, namely, MAPprox, It is shown that MAPprox also provides minimum variance estimates under reasonable assumptions.

  • 47.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    A Linear Regression Approach to State-Space Subspace System Identification1996In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 52, no 2, p. 103-129Article in journal (Refereed)
    Abstract [en]

    Recently, state-space subspace system identification (4SID) has been suggested as an alternative to the more traditional prediction error system identification. The aim of this paper is to analyze the connections between these two different approaches to system identification. The conclusion is that 4SID can be viewed as a linear regression multistep-ahead prediction error method with certain rank constraints. This allows us to describe 4SID methods within the standard framework of system identification and linear regression estimation. For example, this observation is used to compare different cost-functions which occur rather implicitly in the ordinary framework of 4SID. From the cost-functions, estimates of the extended observability matrix are derived and related to previous work. Based on the estimates of the observability matrix, the asymptotic properties of two pole estimators, namely the shift invariance method and a weighted subspace fitting method, are analyzed. Expressions for the asymptotic variances of the pole estimation error are given. From these expressions, difficulties in choosing user-specified parameters are pointed out. Furthermore, it is found that a row-weighting in the subspace estimation step does not affect the pole estimation error asymptotically.

  • 48.
    Jansson, Magnus
    et al.
    KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Wahlberg, Bo
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Counterexample to General Consistency of Subspace System Identification Methods1997In: Proceedings of 11th IFAC Symposium on System Identification, 1997Conference paper (Refereed)
  • 49.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On Consistency of Subspace Methods for System Identification1998In: Automatica, ISSN 00051098, Vol. 34, no 12, p. 1507-1519Article in journal (Refereed)
    Abstract [en]

    Subspace methods for identification of linear time-invariant dynamical systems typically consist of two main steps: a so-called subspace estimate, and an estimate of system parameters based on the subspace estimate. In this paper, the consistency of a large class of methods for estimating the extended observability matrix is analyzed. Persistence of excitation conditions on the input signal are given which guarantee consistent estimates for systems with only measurement noise. For systems with process noise, it is shown that a persistence of excitation condition on the input is not sufficient. More precisely, an example for which the subspace methods fail to give a consistent estimate of the transfer function is given. This failure occurs even if the input is persistently exciting of any order. It is also shown that this problem can be eliminated if stronger conditions on the input signal are imposed.

  • 50.
    Jansson, Magnus
    et al.
    KTH, Superseded Departments, Signals, Sensors and Systems.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control.
    On Consistency of Subspace System Identification Methods1996In: Reglermöte, 1996, p. 95-99Conference paper (Refereed)
1234 1 - 50 of 153
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