An overview of beamforming from a signal-processing perspective is provided, with an emphasis on recent research. Data-independent, statistically optimum, adaptive, and partially adaptive beamforming are discussed. Basic notation, terminology, and concepts are included. Several beamformer implementations are briefly described. >

Beamforming: a versatile approach to spatial filtering

In recent years there has been much interest in robustness issues in general and in robust signal processing schemes in particular. Robust schemes are useful in situations where imprecise a priori knowledge of input characteristics makes the sensitivity of performance to deviations from assumed conditions an important factor in the design of good signal processing schemes. In this survey we discuss the minimax approach for the design of robust methods for signal processing. This has proven to be a very useful approach because it leads to constructive procedures for designing robust schemes. Our emphasis is on the contributions which have been made in robust signal processing, although key results of other robust statistical procedures are also considered. Most of the results we survey have been obtained in the past fifteen years, although some interesting earlier ideas for minimax signal processing are also mentioned. This survey is organized into five main parts, which deal separately with robust linear filters for signal estimation, robust linear filters for signal detection and related applications, nonlinear methods for robust signal detection, nonlinear methods for robust estimation, and robust data quantization. The interrelationships among many of these results are also discussed in the survey.

Robust techniques for signal processing: A survey

This tutorial presents the application of wavelet transforms to wideband correlation processing. One major difference between most applications of wavelets and the work presented is the choice of mother wavelet. It focuses on nonorthogonal, continuous mother wavelets, whereas most applications use the orthogonal mother wavelets that were advanced by Daubechies (1988). The continuous wavelet transform then provides an additional tool for studying and gaining insight into wideband correlation processing. In order to understand when wideband processing may be required, its counterpart, narrowband processing, is presented and its limitations are discussed. Identifying those applications requiring wideband processing and presenting techniques to implement the processing are two of the goals of this tutorial article. The underlying tool is the wavelet transform. >

Wavelets and wideband correlation processing

Real-time ultrasonic imaging systems have been available for more than thirty years. During this time much has occurred to the basic architecture and functions of these clinical systems and their beamformers, which are, in many ways, the most important components of these systems. This talk will review some of the changes that have occurred and will discuss current trends in beamformer design. Throughout most of the 30 years of real time imaging, analog beamformers have been the mainstay of all instruments. The common methods for the implementation of analog beamformers for annular, linear/curved arrays, and phased arrays will be reviewed and the distinguishing characteristics identified. At the present time the industry is undergoing a major shift toward digital beamformation with the introduction of several commercial systems. Given that the earliest digital systems were available roughly 15 years ago, it is fair to say that the introduction of digital beamformers has been relatively slow. Today this shift seems to be gaining momentum. Some of the factors causing these shifts will be reviewed along the the common types of designs and implementations. The ongoing search for improved imaging performance will continue to introduce new challenges for beamformer designers. Among already proposed imaging methods and techniques are elevation focusing (1.5D arrays), synthetic apertures, 2D and sparse arrays, phase aberration correction, and others. The most common complication introduced by these is a significant increase in channel count. Further, there will also be beamformer design issues related to signal processing. These include not only Doppler processing but also the processing of signals due to novel contrast agents. Finally, with advances in computer and microelectronics technologies, the way we view beamformation may have to undergo sizeable changes. These topics will be reviewed not only as technical challenges but also in light of the constraints introduced by today's marketplace.

Evolution of ultrasound beamformers

We present a compressive wide-band spectrum sensing scheme for cognitive radios. The received analog signal at the cognitive radio sensing receiver is transformed in to a digital signal using an analog-to-information converter. The autocorrelation of this compressed signal is then used to reconstruct an estimate of the signal spectrum. We evaluate the performance of this scheme in terms of the mean squared error of the power spectrum density estimate and the probability of detecting signal occupancy.

Compressive wide-band spectrum sensing

This paper is a tutorial which describes "main stream" sonar digital signal processing functions along with the associated implementation considerations. The attempt is to promote further cross-fertilization of ideas among digital signal processing applications in sonar, radar, speech, communications, seismology, and other related fields.

R.G. Pridham

Papers

Digital signal processing for sonar