Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce cochannel interferences and pointing independent beams toward various mobiles, as well as its ability to provide estimates of directions of radiating sources, make it attractive to a mobile communications system designer. Array processing is expected to play an important role in fulfilling the increased demands of various mobile communications services. Part I of this paper showed how an array could be utilized in different configurations to improve the performance of mobile communications systems, with references to various studies where feasibility of apt array system for mobile communications is considered. This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them. This paper brings together almost all aspects of array signal processing.

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Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations

Introduction to linear and nonlinear programming

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

System Identification I

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA.

Survey on Independent Component Analysis

Recursive prediction error identification using the nonlinear Wiener model

Recursive identification algorithms, based on the nonlinear Wiener model, are presented. A recursive identification algorithm is first derived from a general parameterization of the Wiener model, using a stochastic approximation framework. Local and global convergence of this algorithm can be tied to the stability properties of an associated differential equation. Since inversion is not utilized, noninvertible static nonlinearities can be handled, which allows a treatment of, for example, saturating sensors and blind adaptation problems. Gauss-Newton and stochastic gradient algorithms for the situation where the static nonlinearity is known are then suggested in the single-input/single-output case. The proposed methods can outperform conventional linearizing inversion of the nonlinearity when measurement disturbances affect the output signal. For FIR (finite impulse response) models, it is also proved that global convergence of the schemes is tied to sector conditions on the static nonlinearity. In particular, global convergence of the stochastic gradient method is obtained, provided that the nonlinearity is strictly monotone. The local analysis, performed for IIR (infinite impulse response) models, illustrates the importance of the amplitude contents of the exciting signals. >

Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model

Cell identity (cell ID) is the backbone positioning method of most cellular-communication systems. The reasons for this include availability wherever there is cellular coverage and an instantaneous response. Due to these advantages, technology that enhances the accuracy of the method has received considerable interest. This paper presents a new adaptive enhanced cell-ID (AECID) localization method. The method first clusters high-precision position measurements, e.g., assisted-GPS measurements. The high-precision position measurements of each cluster are tagged with the same set of detectable neighbor cells, auxiliary connection information (e.g., the radio-access bearer), as well as quantized auxiliary measurements [e.g., roundtrip time]. The algorithm proceeds by computation and tagging of a polygon of minimal area that contains a prespecified fraction of the high-precision position measurements of each tagged cluster. A novel algorithm for calculation of a polygon is proposed for this purpose. Whenever AECID positioning is requested, the method first looks up the detected neighbor cells and the auxiliary connection information and performs required auxiliary measurements. The polygon corresponding to the so-obtained tag is then retrieved and sent in response to the positioning request. The automatic self-learning algorithm provides location results in terms of minimal areas with a guaranteed confidence, adapted against live measurements. The AECID method can, therefore, also be viewed as a robust fingerprinting algorithm. The application to fingerprinting is illustrated by an example where quantized path-loss measurements from six base stations are combined.

Adaptive Enhanced Cell-ID Fingerprinting Localization by Clustering of Precise Position Measurements

On identification of FIR systems having quantized output data

This brief presents new modeling and identification strategies to address many difficulties in the identification of anesthesia dynamics. The most commonly used models for the effect of muscle relaxants during general anesthesia comprise a high number (greater than eight) of pharmacokinetic and pharmacodynamic parameters. The main issue concerning the neuromuscular blockade system identification is that, in the clinical practice, the input signals (drug dose profiles to be administered to the patients) vary too little to provide a sufficient excitation of the system. The limited amount of measurement data also indicates a need for new identification strategies. A new single-input single-output Wiener model with two parameters is hence proposed to model the effect of atracurium. An extended Kalman filter approach is used to perform the online identification of the system parameters. This approach outperforms many conventional identification strategies, and shows good results regarding parameter identification and measured signal tracking, when evaluated on a large patient database. The new method proved to be adequate for the description of the system, even with the poor input signal excitation and the few measured data samples present in this application. It turns out that the method is of general validity for the identification of drug dynamics in the human body.

Torbjörn Wigren

Papers

Recursive prediction error identification using the nonlinear Wiener model

Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model

Adaptive Enhanced Cell-ID Fingerprinting Localization by Clustering of Precise Position Measurements

On identification of FIR systems having quantized output data

Nonlinear Identification of a Minimal Neuromuscular Blockade Model in Anesthesia