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Identification and compensation of Wiener-Hammerstein systems with feedback

Published in:
ICASSP 2011, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 22-27 May 2011, pp. 4056-4059.

Summary

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or feedback filter are more tractable but more difficult to identify. In this paper, we extend a Wiener-Hammerstein identification method to such systems showing that identification is possible (up to inherent model ambiguities) from single- and two-tone measurements. We also calculate the Cramer-Rao bound for the system parameters and compare to our identification method in simulation. Finally, we demonstrate equalization performance using measured data from a wideband GaN power amplifier.
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Summary

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or...

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Subgraph detection using eigenvector L1 norms

Published in:
23rd Int. Conf. on Neural Info. Process. Syst., NIPS, 6-9 December 2010, pp. 1633-41.

Summary

When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies. Nevertheless, it is desirable to determine the detectability of small, anomalous graphs embedded into background networks with known statistical properties. Casting the problem of subgraph detection in a signal processing context, this article provides a framework and empirical results that elucidate a "detection theory" for graph-valued data. Its focus is the detection of anomalies in unweighted, undirected graphs through L1 properties of the eigenvectors of the graph's so-called modularity matrix. This metric is observed to have relatively low variance for certain categories of randomly-generated graphs, and to reveal the presence of an anomalous subgraph with reasonable reliability when the anomaly is not well-correlated with stronger portions of the background graph. An analysis of subgraphs in real network datasets confirms the efficacy of this approach.
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Summary

When working with network datasets, the theoretical framework of detection theory for Euclidean vector spaces no longer applies. Nevertheless, it is desirable to determine the detectability of small, anomalous graphs embedded into background networks with known statistical properties. Casting the problem of subgraph detection in a signal processing context, this...

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Physical layer considerations for wideband cognitive radio

Published in:
MILCOM 2010, IEEE Military Communications Conference , 31 October-3 November 2010, pp. 2113-2118.

Summary

Next generation cognitive radios will benefit from the capability of transmitting and receiving communications waveforms across many disjoint frequency channels spanning hundreds of megahertz of bandwidth. The information theoretic advantages of multi-channel operation for cognitive radio (CR), however, come at the expense of stringent linearity requirements on the analog transmit and receive hardware. This paper presents the quantitative advantages of multi-channel operation for next generation CR, and the advanced digital compensation algorithms to mitigate transmit and receive nonlinearities that enable broadband multi-channel operation. Laboratory measurements of the improvement in the performance of a multi-channel CR communications system operating below 2 GHz in over 500 MHz of instantaneous bandwidth are presented.
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Summary

Next generation cognitive radios will benefit from the capability of transmitting and receiving communications waveforms across many disjoint frequency channels spanning hundreds of megahertz of bandwidth. The information theoretic advantages of multi-channel operation for cognitive radio (CR), however, come at the expense of stringent linearity requirements on the analog transmit...

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Terminal Doppler Weather Radar enhancements

Author:
Published in:
IEEE Radar Conf., 10 May 2010, pp. 1245-1249.

Summary

The design of an open radar data acquisition system for the Terminal Doppler Weather Radar is presented. Adaptive signal transmission and processing techniques that take advantage of the enhanced capabilities of this new system are also discussed. Results displaying data quality improvements with respect to problems such as range-velocity ambiguity and moving clutter are shown.
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Summary

The design of an open radar data acquisition system for the Terminal Doppler Weather Radar is presented. Adaptive signal transmission and processing techniques that take advantage of the enhanced capabilities of this new system are also discussed. Results displaying data quality improvements with respect to problems such as range-velocity ambiguity...

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Signal processing algorithms for the Terminal Doppler Weather Radar: Build 2

Author:
Published in:
MIT Lincoln Laboratory Report ATC-363

Summary

As a new radar data acquisition system (RDA) was developed for the Terminal Doppler Weather Radar (TDWR), enhanced signal processing algorithms taking advantage of its increased capabilities were also developed. The primary goals of protecting the base data estimates from range-aliased signals and providing reliable velocity dealiasing were achieved through multiple pulse repetition interval (PRI) and phase coding methods. An innovative radial-by-radial adaptive selection process was used to take full advantage of the different techniques, the first time such an approach has been implemented for weather radars. Improvement in clutter filtering was also achieved. This report discusses in detail these new RDA signal processing algorithms.
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Summary

As a new radar data acquisition system (RDA) was developed for the Terminal Doppler Weather Radar (TDWR), enhanced signal processing algorithms taking advantage of its increased capabilities were also developed. The primary goals of protecting the base data estimates from range-aliased signals and providing reliable velocity dealiasing were achieved through...

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Toward signal processing theory for graphs and non-Euclidean data

Published in:
ICASSP 2010, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 15 March 2010, pp. 5415-5417.

Summary

Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be developed. In this paper we show the conceptual advantages gained by formulating graph analysis problems in a signal processing framework by way of a practical example: detection of a subgraph embedded in a background graph. We describe an approach based on detection theory and provide empirical results indicating that the test statistic proposed has reasonable power to detect dense subgraphs in large random graphs.
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Summary

Graphs are canonical examples of high-dimensional non-Euclidean data sets, and are emerging as a common data structure in many fields. While there are many algorithms to analyze such data, a signal processing theory for evaluating these techniques akin to detection and estimation in the classical Euclidean setting remains to be...

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High-pitch formant estimation by exploiting temporal change of pitch

Published in:
Proc. IEEE Trans. on Audio, Speech, and Language Processing, Vol. 18, No. 1, January 2010, pp. 171-186.

Summary

This paper considers the problem of obtaining an accurate spectral representation of speech formant structure when the voicing source exhibits a high fundamental frequency. Our work is inspired by auditory perception and physiological studies implicating the use of pitch dynamics in speech by humans. We develop and assess signal processing schemes aimed at exploiting temporal change of pitch to address the high-pitch formant frequency estimation problem. Specifically, we propose a 2-D analysis framework using 2-D transformations of the time-frequency space. In one approach, we project changing spectral harmonics over time to a 1-D function of frequency. In a second approach, we draw upon previous work of Quatieri and Ezzat et al. [1], [2], with similarities to the auditory modeling efforts of Chi et al. [3], where localized 2-D Fourier transforms of the time-frequency space provide improved source-filter separation when pitch is changing. Our methods show quantitative improvements for synthesized vowels with stationary formant structure in comparison to traditional and homomorphic linear prediction. We also demonstrate the feasibility of applying our methods on stationary vowel regions of natural speech spoken by high-pitch females of the TIMIT corpus. Finally, we show improvements afforded by the proposed analysis framework in formant tracking on examples of stationary and time-varying formant structure.
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Summary

This paper considers the problem of obtaining an accurate spectral representation of speech formant structure when the voicing source exhibits a high fundamental frequency. Our work is inspired by auditory perception and physiological studies implicating the use of pitch dynamics in speech by humans. We develop and assess signal processing...

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A multi-sensor compressed sensing receiver: performance bounds and simulated results

Published in:
43rd Asilomar Conf. on Signals, Systems, and Computers, 1-4 November 2009, pp. 1571-1575.

Summary

Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present a multi-sensor compressive sensing receiver that is capable of reconstructing frequency-sparse signals using block reconstruction techniques in a sensor-frequency basis. We derive performance bounds for time-difference and angle of arrival (AoA) estimation of such a receiver, and present simulated results in which we compare AoA reconstruction performance to the bounds derived.
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Summary

Multi-sensor receivers are commonly tasked with detecting, demodulating and geolocating target emitters over very wide frequency bands. Compressed sensing can be applied to persistently monitor a wide bandwidth, given that the received signal can be represented using a small number of coefficients in some basis. In this paper we present...

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Rapid prototyping of radar algorithms

Author:
Published in:
IEEE Sig. Proc. Mag., Vol. 26, No. 6, November 2009, pp. 158-162.

Summary

Rapid prototyping of advanced signal processing algorithms is critical to developing new radars. Signal processing engineers usually use high level languages like MATLAB, IDL, or Python to develop advanced algorithms and to determine the optimal parameters for these algorithms. Many of these algorithms have very long execution times due to computational complexity and/or very large data sets, which hinders an efficient engineering development workflow. That is, signal processing engineers must wait hours, or even days, to get the results of the current algorithm, parameters, and data set before making changes and refinements for the next iteration. In the meantime, the engineer may have thought of several more permutations that he or she wants to test.
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Summary

Rapid prototyping of advanced signal processing algorithms is critical to developing new radars. Signal processing engineers usually use high level languages like MATLAB, IDL, or Python to develop advanced algorithms and to determine the optimal parameters for these algorithms. Many of these algorithms have very long execution times due to...

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Sinewave parameter estimation using the fast Fan-Chirp Transform

Published in:
Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA, 18-21 October 2009, pp. 349-352.

Summary

Sinewave analysis/synthesis has long been an important tool for audio analysis, modification and synthesis [1]. The recently introduced Fan-Chirp Transform (FChT) [2,3] has been shown to improve the fidelity of sinewave parameter estimates for a harmonic audio signal with rapid frequency modulation [4]. A fast version of the FChT [3] reduces computation but this algorithm presents two factors that affect sinewave parameter estimation. The phase of the fast FChT does not match the phase of the original continuous-time transform and this interferes with the estimation of sinewave phases. Also, the fast FChT requires an interpolation of the input signal and the choice of interpolator affects the speed of the transform and accuracy of the estimated sinewave parameters. In this paper we demonstrate how to modify the phase of the fast FChT such that it can be used to estimate sinewave phases, and we explore the use of various interpolators demonstrating the tradeoff between transform speed and sinewave parameter accuracy.
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Summary

Sinewave analysis/synthesis has long been an important tool for audio analysis, modification and synthesis [1]. The recently introduced Fan-Chirp Transform (FChT) [2,3] has been shown to improve the fidelity of sinewave parameter estimates for a harmonic audio signal with rapid frequency modulation [4]. A fast version of the FChT [3]...

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