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Mats Viberg

Bio: Mats Viberg is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Sensor array & Estimation theory. The author has an hindex of 41, co-authored 231 publications receiving 11749 citations. Previous affiliations of Mats Viberg include Linköping University & Blekinge Institute of Technology.


Papers
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Journal ArticleDOI
TL;DR: Despite not exploiting the model, the variance of the proposed non-parametric estimators is found to be close to the Cramer-Rao lower bound for Gaussian spreads.

1 citations

Journal ArticleDOI
TL;DR: The article is prompted by a paper written by Liang, Wilkes and Cadzow which discussed the maximum likelihood (ML) estimation of difference equation parameters in a flawed manner and concludes that all ML-based order estimation procedures yield results that do not depend on the normalizing constraint imposed on the parameter vector.
Abstract: The article is prompted by a paper written by Liang, Wilkes and Cadzow (see ibid., vol.41, p. 3003-3009, 1993) which discussed the maximum likelihood (ML) estimation of difference equation parameters in a flawed manner. The correct ML parameter estimate is derived herein by means of a high-level argument based on the invariance principle as well as by a direct calculation. Contrary to what is suggested in the aforementioned paper, all ML-based order estimation procedures (such as AIC or GAIC rules) yield results that do not depend on the normalizing constraint imposed on the parameter vector. >

1 citations

01 Jan 2001
TL;DR: In this paper, a realistic MIMO spatiotemporal radio channel model for simulation of multi-element antenna (MEA) systems is presented, based on fundamental theory from ElectroMagnetic (EM) scattering where the properties of the transmit and receive antennas are included.
Abstract: A realistic Multi-Input Multi-Output (MIMO) spatiotemporal radio channel model for simulation of MultiElement Antenna (MEA) systems is presented. The model is based on fundamental theory from ElectroMagnetic (EM) scattering where the properties of the transmit and receive antennas are included. Both the direction and amplitude of the electromagnetic field are included in the model. Furthermore, mutual coupling between the antenna elements is also accounted for since the typical MIMO system most likely will use closely spaced antenna elements. Surprisingly, it is found that mutual coupling actually decorrelates the received signals. Thus, mutual coupling may, contrary to common belief, sometimes increase the communication capacity.

1 citations

Proceedings ArticleDOI
11 Apr 1988
TL;DR: The author investigates the optimal output SNR (signal-to-noise ratio) for adaptive arrays and concludes that the average optimal SNR increases linearly with the number of reflections of the desired signal.
Abstract: The author investigates the optimal output SNR (signal-to-noise ratio) for adaptive arrays. His main interest is what happens in the case of coherent multipath propagation. He casts the multipath into a statistical framework and derives an approximation of the average optimal SNR. He concludes that the average optimal SNR increases linearly with the number of reflections of the desired signal. The reason for this is that it is possible for the receiver to take advantage of the extra desired signal energy that comes from the reflections. >

1 citations

Proceedings ArticleDOI
14 Sep 1998
TL;DR: In this paper, a pole estimation method based on Kung's algorithm, a model order estimation scheme by bootstrap/SVD, and an SVD-based change detector are proposed.
Abstract: This paper presents a scheme for estimation of multiple overlapping transients in additive white noise using a single sensor. The scheme is based on three main ideas: a pole estimation method based on Kung's algorithm; a model order estimation scheme by bootstrap/SVD; and an SVD-based change detector. By combining these schemes it is demonstrated that multiple transients with different starting points can be estimated. The method is tested on a number of simulations. The order estimation method is shown to be more accurate than AIC, for high SNR and short segments.

1 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors examined the performance of using multi-element array (MEA) technology to improve the bit-rate of digital wireless communications and showed that with high probability extraordinary capacity is available.
Abstract: This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, n, of antenna elements at both transmitter and receiver. We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon‘s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.

10,526 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: The article consists of background material and of the basic problem formulation, and introduces spectral-based algorithmic solutions to the signal parameter estimation problem and contrast these suboptimal solutions to parametric methods.
Abstract: The quintessential goal of sensor array signal processing is the estimation of parameters by fusing temporal and spatial information, captured via sampling a wavefield with a set of judiciously placed antenna sensors. The wavefield is assumed to be generated by a finite number of emitters, and contains information about signal parameters characterizing the emitters. A review of the area of array processing is given. The focus is on parameter estimation methods, and many relevant problems are only briefly mentioned. We emphasize the relatively more recent subspace-based methods in relation to beamforming. The article consists of background material and of the basic problem formulation. Then we introduce spectral-based algorithmic solutions to the signal parameter estimation problem. We contrast these suboptimal solutions to parametric methods. Techniques derived from maximum likelihood principles as well as geometric arguments are covered. Later, a number of more specialized research topics are briefly reviewed. Then, we look at a number of real-world problems for which sensor array processing methods have been applied. We also include an example with real experimental data involving closely spaced emitters and highly correlated signals, as well as a manufacturing application example.

4,410 citations

Journal ArticleDOI
01 Nov 2007
TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Abstract: Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

4,123 citations

Book ChapterDOI
01 Jan 2011
TL;DR: Weakconvergence methods in metric spaces were studied in this article, with applications sufficient to show their power and utility, and the results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables.
Abstract: The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

3,554 citations