Adaptive beamformer

About: Adaptive beamformer is a(n) research topic. Over the lifetime, 4934 publication(s) have been published within this topic receiving 93100 citation(s).

Adaptive Signal Processing

Abstract: GENERAL INTRODUCTION. Adaptive Systems. The Adaptive Linear Combiner. THEORY OF ADAPTATION WITH STATIONARY SIGNALS. Properties of the Quadratic Performance Surface. Searching the Performance Surface. Gradient Estimation and Its Effects on Adaptation. ADAPTIVE ALGORITHMS AND STRUCTURES. The LMS Algorithm. The Z-Transform in Adaptive Signal Processing. Other Adaptive Algorithms and Structures. Adaptive Lattice Filters. APPLICATIONS. Adaptive Modeling and System Identification. Inverse Adaptive Modeling, Deconvolution, and Equalization. Adaptive Control Systems. Adaptive Interference Cancelling. Introduction to Adaptive Arrays and Adaptive Beamforming. Analysis of Adaptive Beamformers.

Beamforming: a versatile approach to spatial filtering

Abstract: 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. >

An algorithm for linearly constrained adaptive array processing

Abstract: A constrained least mean-squares algorithm has been derived which is capable of adjusting an array of sensors in real time to respond to a signal coming from a desired direction while discriminating against noises coming from other directions. Analysis and computer simulations confirm that the algorithm is able to iteratively adapt variable weights on the taps of the sensor array to minimize noise power in the array output. A set of linear equality constraints on the weights maintains a chosen frequency characteristic for the array in the direction of interest. The array problem would be a classical constrained least-mean-squares problem except that the signal and noise statistics are assumed unknown a priori. A geometrical presentation shows that the algorithm is able to maintain the constraints and prevent the accumulation of quantization errors in a digital implementation.

Array Signal Processing: Concepts and Techniques

Abstract: 1. Introduction 2. Signals in Space and Time 3. Apertures and Arrays 4. Conventional Array Processing 5. Detection Theory 6. Estimation Theory 7. Adaptive Array Processing 8. Tracking Appendices References List of Symbols Index.