Institution
Applied Signal Technology, Inc.
About: Applied Signal Technology, Inc. is a based out in . It is known for research contribution in the topics: Adaptive filter & Demodulation. The organization has 105 authors who have published 130 publications receiving 3298 citations.
Topics: Adaptive filter, Demodulation, Multipath propagation, Signal processing, Quadrature amplitude modulation
Papers published on a yearly basis
Papers
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01 Jan 1987TL;DR: In this article, the FIR Linear Combiner Number Guessing Games Single-Integer Guessing Game Multiple-Integer Games Multiple Real Numbers Guessing games Adaptive Filter Algorithm Interpretation AN ANALYTICAL FRAMEWORK for DEVELOPNG ADAPTIVE ALGORITHMS: Background and Direction The Least Squares Problem Basic Formulation Reduction to the Normal Equations Direct Solution to the Optimal Vector The Meaning of P and P Examples Matching a Pure Delay Matching an Rotated Phasor Two Solution Technique Direct Inversion Iterative App
Abstract: THE NEED FOR ADAPTIVE FILTERING: Removal of Power Line Hum From Medical Instruments Adaptive Differential Pulse Code Modulation Equalization of Troposcatter Communication Signals Generality and Commonality BASIC PRINCIPLES OF ADAPTIVE FILTERING: The FIR Linear Combiner Number Guessing Games Single-Integer Guessing Games Multiple- Integer Guessing Games Multiple Real Numbers Guessing Games Adaptive Filter Algorithm Interpretation AN ANALYTICAL FRAMEWORK FOR DEVELOPNG ADAPTIVE ALGORITHMS: Background and Direction The Least Squares Problem Basic Formulation Reduction to the Normal Equations Direct Solution to the Optimal Vector The Meaning of P and P Examples Matching a Pure Delay Matching a Rotated Phasor Two Solution Technique Direct Inversion Iterative Approximation Computational Comparisons Consolidation The Least Mean Square Problem Formulation A Brief Review of Stochastic Process Ensemble Averages An Example Stationarity The Concept of White Noise.
383 citations
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07 Apr 1986TL;DR: This paper introduces a new adaptive beam-forming algorithm called the CM Array, which exploits the constant modulus property of the signal of interest to steer a beam in the direction of the soi while steering nulls in the directions of interference.
Abstract: This paper introduces a new adaptive beam-forming algorithm called the CM Array. Unlike existing adaptive beamformers, the adaptive CM Array exploits the constant modulus (cm) property of the signal of interest (soi) to steer a beam in the direction of the soi while steering nulls in the directions of interference.
295 citations
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TL;DR: A real input, real coefficient version of the constant modulus algorithm is shown to perform arbitrarily close to the fully complex version, extended for the enhancement of signals having a nonconstant but known envelope, as might arise in data signals with pulse shaping.
Abstract: This paper presents three extensions of the constant modulus algorithm (CMA) introduced in an earlier paper as a means of correcting degradations in constant enyelope waveforms. As originally formulated, the CMA employs an FIR filter with complex coefficients and accepts complex (quadrature) input data. In this paper, first a real input, real coefficient version of the algorithm is shown to perform arbitrarily close to the fully complex version. Secondly, the algorithm is extended for the enhancement of signals having a nonconstant but known envelope, as might arise in data signals with pulse shaping. Lastly, a multichannel version of CMA, wherein several observations are linearly combined, is presented for joint adaptation of multiple filters. This approach can be used, for example, as a means of spatial or polarization "beamsteering" to reject additive interferers and compensate for channel-induced polarization rotation.
287 citations
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TL;DR: The data communications problem is described, the rationale for introducing fractionally spaced equalizers, new results, and their implications are described, and results are applied to actual transmission channels.
Abstract: Modern digital transmission systems commonly use an adaptive equalizer as a key part of the receiver. The design of this equalizer is important since it determines the maximum quality attainable from the system, and represents a high fraction of the computation used to implement the demodulator. Analytical results offer a new way of looking at fractionally spaced equalizers and have some surprising practical implications. This article describes the data communications problem, the rationale for introducing fractionally spaced equalizers, new results, and their implications. We then apply those results to actual transmission channels.
212 citations
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TL;DR: This paper examines the use of CS in the design of a wideband radio receiver in a noisy environment and concludes that while a CS-based system has inherent limitations that do impose some restrictions on its potential applications, it also has attributes that make it highly desirable in a number of important practical settings.
Abstract: Compressive sensing (CS) exploits the sparsity present in many signals to reduce the number of measurements needed for digital acquisition. With this reduction would come, in theory, commensurate reductions in the size, weight, power consumption, and/or monetary cost of both signal sensors and any associated communication links. This paper examines the use of CS in the design of a wideband radio receiver in a noisy environment. We formulate the problem statement for such a receiver and establish a reasonable set of requirements that a receiver should meet to be practically useful. We then evaluate the performance of a CS-based receiver in two ways: via a theoretical analysis of its expected performance, with a particular emphasis on noise and dynamic range, and via simulations that compare the CS receiver against the performance expected from a conventional implementation. On the one hand, we show that CS-based systems that aim to reduce the number of acquired measurements are somewhat sensitive to signal noise, exhibiting a 3 dB SNR loss per octave of subsampling, which parallels the classic noise-folding phenomenon. On the other hand, we demonstrate that since they sample at a lower rate, CS-based systems can potentially attain a significantly larger dynamic range. Hence, we conclude that while a CS-based system has inherent limitations that do impose some restrictions on its potential applications, it also has attributes that make it highly desirable in a number of important practical settings.
173 citations
Authors
Showing all 105 results
Name | H-index | Papers | Citations |
---|---|---|---|
Chris R. Johnson | 58 | 485 | 14316 |
Laura Balzano | 29 | 111 | 3746 |
J.R. Treichler | 14 | 48 | 1373 |
Anjana K. Shah | 13 | 39 | 513 |
Michael J. Ready | 7 | 15 | 252 |
Jonathan Wintrode | 6 | 14 | 89 |
Jonathan S. Leary | 6 | 8 | 326 |
R.P. Gooch | 4 | 4 | 431 |
R.P. Gooch | 4 | 13 | 96 |
R. Gooch | 4 | 7 | 89 |
David H. Detienne | 3 | 6 | 150 |
Jerry R. Hinson | 3 | 5 | 19 |
Ernest T. Tsui | 3 | 3 | 246 |
M.G. Larimore | 3 | 5 | 15 |
Andy Wilby | 3 | 4 | 19 |