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

Digital processing of speech signals

This paper describes the synthesis method of linear array geometry with minimum sidelobe level and null control using the particle swarm optimization (PSO) algorithm. The PSO algorithm is a newly discovered, high-performance evolutionary algorithm capable of solving general N-dimensional, linear and nonlinear optimization problems. Compared to other evolutionary methods such as genetic algorithms and simulated annealing, the PSO algorithm is much easier to understand and implement and requires the least of mathematical preprocessing. The array geometry synthesis is first formulated as an optimization problem with the goal of sidelobe level (SLL) suppression and/or null placement in certain directions, and then solved by the PSO algorithm for the optimum element locations. Three design examples are presented that illustrate the use of the PSO algorithm, and the optimization goal in each example is easily achieved. The results of the PSO algorithm are validated by comparing with results obtained using the quadratic programming method (QPM).

Linear array geometry synthesis with minimum sidelobe level and null control using particle swarm optimization

A simple and flexible genetic algorithm (GA) for pattern synthesis of antenna array with arbitrary geometric configuration is presented. Unlike conventional GA using binary coding and binary crossover, this approach directly represents the array excitation weighting vectors as complex number chromosomes and uses decimal linear crossover without a crossover site. Compared with conventional GAs, this approach has a few advantages: giving a clearer and simpler representation of the problem, simplifying chromosome construction, and totally avoiding binary encoding and decoding so as to simplify software programming and to reduce CPU time. This method also allows us to impose constraints on phases and magnitudes of complex excitation coefficients for preferable implementation in practice using digital phase shifters and digital attenuators. Successful applications show that the approach can be used as a general tool for pattern synthesis of arbitrary arrays.

Sidelobe reduction in array-pattern synthesis using genetic algorithm

dix A. Even if you don’t choose to memorize them this system aids in reference and retreival of important formulas. The book was compiled from notes developed during eight years of teaching a graduate course on the subject and was used as a text. Thus it has been student tested. Appendix F contains a number of problems, grouped to be used on a chapter by chapter basis The problems are designed to illustrate practical applications of the text material. The parts of this book of most interest and value to the EMC engineer will be the chapters on Thermal Noise, Antennas, Propagation and Transmission Lines, and  Reflection and Refraction. This is not  to downpade the chapters on Statistics and Its Applications, Signal Processing and Detection, and Some System Characteristics which also contain much potentially useful materials. Additional plus values for the book include a list of 40 references, a table of symbols used throughout the book, and a subject index. Some readers may find the condensed type and close line spacing hard to read. It was apparently set up by typewriter using an elite type face with single line spacing. When reduced down to a 6 by 9 5 inch size page it is too crowded for easy reading. In spite of this shortcoming your reviewer recommends this book as a worthwhile reference in this field of interest.

http://ieeexplore.ieee.org/iel5/5/31269/01455368.pdf

Signal analysis

In array signal processing, high-resolution direction-of-arrival (DOA) estimation algorithms are known to be sensitive to system errors. In practice, the system should be properly calibrated before DOA estimation. In this paper, a calibration method of gain and phase errors of linear equispaced arrays (LEAs) is considered. A class of simplified calibration algorithms based on different diagonal lines of the covariance matrix is proposed. The statistical performance analyses of the calibration algorithms due to finite data perturbations are presented. Expressions for average bias (Abias) and square root of average variance (SRAV) of the calibration algorithms are derived using first-order approximation. These statistical and computer simulation results reveal and explain why the more diagonal lines that are used for gain and phase error estimations, the more inferior performance that may be obtained. Based on this conclusion, the simple and optimal gain and phase error calibration algorithms for such model are obtained.

Theoretical analyses of gain and phase error calibration with optimal implementation for linear equispaced array

In this paper, novel robust adaptive beamformers are proposed with constraints on array magnitude response. With the transformation from the array output power and the magnitude response to linear functions of the autocorrelation sequence of the array weight, the optimization of an adaptive beamformer, which is often described as a quadratic optimization problem in conventional beamforming methods, is then reformulated as a linear programming (LP) problem. Unlike conventional robust beamformers, the proposed method is able to flexibly control the robust response region with specified beamwidth and response ripple. In practice, an array has many imperfections besides steering direction error. In order to make the adaptive beamformer robust against all kinds of imperfections, worst-case optimization is exploited to reconstruct the robust beamformer. By minimizing array output power with the existence of the worst-case array imperfections, the robust beamforming can be expressed as a second-order cone programming (SOCP) problem. The resultant beamformer possesses superior robustness against arbitrary array imperfections. With the proposed methods, a large robust response region and a high signal-to-interference-plus-noise ratio (SINR) enhancement can be achieved readily. Simple implementation, flexible performance control, as well as significant SINR enhancement, support the practicability of the proposed methods.

Robust Adaptive Beamformers Based on Worst-Case Optimization and Constraints on Magnitude Response

A calibration technique is proposed in this paper for an arbitrary array. This technique estimates the array sensor gain/phase and geometry with a set of simultaneous equations formed by using the MUSIC null spectrum property. Note that the technique does not use iterative calculation in estimating the array parameters and hence it has no convergent problem; however, it requires that n directions of arrival (DOAs) of signal sources to be known to calibrate the array which is perturbed n-dimensionally. The efficacy of the method is demonstrated by means of simulations and on experimental data collected with an antenna array operating in high-frequency radio band.

A Practical Simple Geometry and Gain/Phase Calibration Technique for Antenna Array Processing

A highly flexible synthesis method for an arbitrary array is proposed to best approximate a desired array pattern in a minimum-mean-square-error sense. The basic idea of the technique is to form a quadratic program with its cost function given by the mean-square error between the array response and a properly selected pattern described by a known mathematical function. This quadratic program can be a constrained or unconstrained optimization problem depending on the requirements of the desired array pattern. In formulating the quadratic program, no assumption has been made on the gain/phase response or characteristics of the individual array elements. Therefore, one can synthesize an array of arbitrary shape to any appropriate pattern with the characteristic of the array elements taken into consideration as long as one is able to model the array accurately. The proposed method is used to synthesize arrays of different shapes, linear as well as planar arrays (including rectangular and circular planar arrays), using a Chebyshev polynomial or zero function as a design template, to illustrate the feasibility of the proposed method. >

A flexible array synthesis method using quadratic programming

In this correspondence, a novel robust adaptive beamformer is proposed based on the worst-case semi-definite programming (SDP). A recent paper has reported that a beamformer robust against large steering direction error can be constructed by using linear constraints on magnitude response in SDP formulation. In practice, however, array system also suffers from many other array imperfections other than steering direction error. In order to make the adaptive beamformer robust against all kinds of array imperfections, the worst-case optimization technique is proposed to reformulate the beamformer by minimizing the array output power with respect to the worst-case array imperfections. The resultant beamformer has the mathematical form of a regularized SDP problem and possesses superior robustness against arbitrary array imperfections. Although the formulation of robust beamformer uses weighting matrix, with the help of spectral factorization approach, the weighting vector can be obtained so that the beamformer can be used for both signal power and waveform estimation. Simple implementation, flexible performance control, as well as significant signal-to-interference-plus-noise ratio (SINR) enhancement, support the practicability of the proposed method.

Meng Hwa Er

Papers

Theoretical analyses of gain and phase error calibration with optimal implementation for linear equispaced array

Robust Adaptive Beamformers Based on Worst-Case Optimization and Constraints on Magnitude Response

A Practical Simple Geometry and Gain/Phase Calibration Technique for Antenna Array Processing

A flexible array synthesis method using quadratic programming

A Robust Adaptive Beamformer Based on Worst-Case Semi-Definite Programming