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.

Detection and Estimation in Sensor Arrays Using

Abstract: This paper addresses the problem of signal param- eter estimation of narrow-band emitter signals impinging on an array of sensors. A multidimensional estimation procedure is proposed, which applies to arbitrary array structures and sig- nal correlation. The method is based on the recently introduced weighted subspace fitting (WSF) criterion, and includes schemes for both detecting the number of sources and estimating the signal parameters. A Gauss-Newton type algorithm is sug- gested for minimizing the WSF criterion. A new detection scheme is also formulated based on the asymptotic distribution of the WSF cost function. Strong consistency of the detection algorithm is proved for arbitrary signal correlation, including coherence. The WSF detection method is compared to a re- cently proposed information theoretic approach, and found to provide a significant improvement for high signal correlation scenarios. Simulations are carried out comparing the proposed WSF technique to the deterministic maximum likelihood (ML) method. The WSF scheme is found to be limited only by the estimation accuracy and not by the initialization or detection. This does not appear to be true for the ML method.

Long-Range Channel Prediction Based on Nonstationary Parametric Modeling

Abstract: Motivated by the analysis of measured radio channels and recently published physics-based scattering SISO and MIMO channel models, a new approach of long-range channel prediction based on nonstationary multicomponent polynomial phase signals (MC-PPS) is proposed. An iterative and recursive method for detecting the number of signals and the orders of the polynomial phases is proposed. The performance of these detectors and estimators is evaluated by Monte Carlo simulations. The performance of the new channel predictors is evaluated using both synthetic signals and examples of real world channels measured in urban and suburban areas. High-order polynomial phase parameters are detected in most of the measured data sets, and the new methods outperform the classical LP in given examples for long-range prediction for the cases where the estimated model parameters are stable. For the more difficult data sets, the performance of these methods are similar, which provides alternatives for system design when other issues are concerned.

Introduction to Array Processing

Abstract: The purpose of this chapter is to give some background material on array signal processing, which serves as a more detailed introduction to the remaining chapters. The ideal data model is introduced and its properties are explored, with a special emphasis on the array response. The general concepts of beamforming and direction-of-arrival estimation are introduced, and exemplified by some well-known techniques. Although the focus is on traditional applications involving an array of coherent sensors, we also present some extensions to non-coherent and/or distributed sensors.

Performance of Subspace-Based System Identification Methods

Abstract: Traditional prediction-error techniques for multivariable system identification require canonical descriptions using a large number of parameters. This problem can be avoided using subspace based m ...

Sensor array processing using gated signals

Abstract: The author considers the problem of using an array of sensors for separating a desired signal from unwanted disturbance signals. The desired signal is assumed to be gated, either in time or in frequency. He derives an eigenstructure based method which requires no knowledge of the array manifold and which allows for coherent signals. >

On Limits of Wireless Communications in a Fading Environment when UsingMultiple Antennas

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.

Pattern Recognition and Machine Learning

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.

Two decades of array signal processing research: the parametric approach

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.

Survey of Wireless Indoor Positioning Techniques and Systems

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.

Convergence of probability measures

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