The random component of mixer-based nonlinear vector network analyzer measurement uncertainty

The uncertainty, due to random noise, of the measurements made with a mixer-based nonlinear vector network analyzer are analyzed. An approximate covariance matrix corresponding to the measurements is derived that can be used for fitting models and maximizing the dynamic range in the measurement setup. The validity of the approximation is verified with measurements

Estimation-based synthesis of H∞-optimal adaptive FIR filtersfor filtered-LMS problems

This paper presents a systematic synthesis procedure for H∞-optimal adaptive FIR filters in the context of an active noise cancellation (ANC) problem. An estimation interpretation of the adaptive control problem is introduced first. Based on this interpretation, an H∞ estimation problem is formulated, and its finite horizon prediction (filtering) solution is discussed. The solution minimizes the maximum energy gain from the disturbances to the predicted (filtered) estimation error and serves as the adaptation criterion for the weight vector in the adaptive FIR filter. We refer to this adaptation scheme as estimation-based adaptive filtering (EBAF). We show that the steady-state gain vector in the EBAF algorithm approaches that of the classical (normalized) filtered-X LMS algorithm. The error terms, however, are shown to be different. Thus, these classical algorithms can be considered to be approximations of our algorithm. We examine the performance of the proposed EBAF algorithm (both experimentally and in simulation) in an active noise cancellation problem of a one-dimensional (1-D) acoustic duct for both narrowband and broadband cases. Comparisons to the results from a conventional filtered-LMS (FxLMS) algorithm show faster convergence without compromising steady-state performance and/or robustness of the algorithm to feedback contamination of the reference signal

Performance bounds on matched-field methods for source localization and estimation of ocean environmental parameters

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2001Matched-field methods concern estimation of source location and/or ocean environmental parameters by exploiting full wave modeling of acoustic waveguide propagation. Typical estimation performance demonstrates two fundamental limitations. First, sidelobe ambiguities dominate the estimation at low signal-to-noise ratio (SNR), leading to a threshold performance behavior. Second, most matched-field algorithms show a strong sensitivity to environmental/system mismatch, introducing some biased estimates at high SNR. In this thesis, a quantitative approach for ambiguity analysis is developed so that different mainlobe and sidelobe error contributions can be compared at different SNR levels. Two large-error performance bounds, the Weiss-Weinstein bound (WWB) and Ziv-Zakai bound (ZZB), are derived for the attainable accuracy of matched-field methods. To include mismatch effects, a modified version of the ZZB is proposed. Performance analyses are implemented for source localization under a typical shallow water environment chosen from the Shallow Water Evaluation Cell Experiments (SWellEX). The performance predictions describe the simulations of the maximum likelihood estimator (MLE) well, including the mean square error in all SNR regions as well as the bias at high SNR. The threshold SNR and bias predictions are also verified by the SWellEX experimental data processing. These developments provide tools to better understand some fundamental behaviors in matched-field performance and provide benchmarks to which various ad hoc algorithms can be compared.Financial support for my research was provided by the Office of Naval Research and the WHOI Education Office

Effects of noise suppression and envelope dynamic range compression on the intelligibility of vocoded sentences for a tonal language

Vocoder simulation studies have suggested that the carrier signal type employed affects the intelligibility of vocoded speech. The present work further assessed how carrier signal type interacts with additional signal processing, namely, single-channel noise suppression and envelope dynamic range compression, in determining the intelligibility of vocoder simulations. In Experiment 1, Mandarin sentences that had been corrupted by speech spectrum-shaped noise (SSN) or two-talker babble (2TB) were processed by one of four single-channel noise-suppression algorithms before undergoing tone-vocoded (TV) or noise-vocoded (NV) processing. In Experiment 2, dynamic ranges of multiband envelope waveforms were compressed by scaling of the mean-removed envelope waveforms with a compression factor before undergoing TV or NV processing. TV Mandarin sentences yielded higher intelligibility scores with normal-hearing (NH) listeners than did noise-vocoded sentences. The intelligibility advantage of noise-suppressed vocoded speech depended on the masker type (SSN vs 2TB). NV speech was more negatively influenced by envelope dynamic range compression than was TV speech. These findings suggest that an interactional effect exists between the carrier signal type employed in the vocoding process and envelope distortion caused by signal processing

MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

This paper proposes a new detection algorithm for MIMO communication systems employing high order QAM constellations. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, a straightforward application of the Belief Propagation (BP) algorithm yields very poor results. Our algorithm is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a loop-free discrete distribution. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the loop-free factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection

Joint data detection and channel estimation for OFDM systems

We develop new blind and semi-blind data detectors and channel estimators for orthogonal frequency-division multiplexing (OFDM) systems. Our data detectors require minimizing a complex, integer quadratic form in the data vector. The semi-blind detector uses both channel correlation and noise variance. The quadratic for the blind detector suffers from rank deficiency; for this, we give a low-complexity solution. Avoiding a computationally prohibitive exhaustive search, we solve our data detectors using sphere decoding (SD) and V-BLAST and provide simple adaptations of the SD algorithm. We consider how the blind detector performs under mismatch, generalize the basic data detectors to nonunitary constellations, and extend them to systems with pilots and virtual carriers. Simulations show that our data detectors perform well

Improving Pure-Tone Audiometry Using Probabilistic Machine Learning Classification

Hearing loss is a critical public health concern, affecting hundreds millions of people worldwide and dramatically impacting quality of life for affected individuals. While treatment techniques have evolved in recent years, methods for assessing hearing ability have remained relatively unchanged for decades. The standard clinical procedure is the modified Hughson-Westlake procedure, an adaptive pure-tone detection task that is typically performed manually by audiologists, costing millions of collective hours annually among healthcare professionals. In addition to the high burden of labor, the technique provides limited detail about an individual’s hearing ability, estimating only detection thresholds at a handful of pre-defined pure-tone frequencies (a threshold audiogram). An efficient technique that produces a detailed estimate of the audiometric function, including threshold and spread, could allow for better characterization of particular hearing pathologies and provide more diagnostic value. Parametric techniques exist to efficiently estimate multidimensional psychometric functions, but are ill-suited for estimation of audiometric functions because these functions cannot be easily parameterized. The Gaussian process is a compelling machine learning technique for inference of nonparametric multidimensional functions using binary data. The work described in this thesis utilizes Gaussian process classification to build an automated framework for efficient, high-resolution estimation of the full audiometric function, which we call the machine learning audiogram (MLAG). This Bayesian technique iteratively computes a posterior distribution describing its current belief about detection probability given the current set of observed pure tones and detection responses. The posterior distribution can be used to provide a current point estimate of the psychometric function as well as to select an informative query point for the next stimulus to be provided to the listener. The Gaussian process covariance function encodes correlations between variables, reflecting prior beliefs on the system; MLAG uses a composite linear/squared exponential covariance function that enforces monotonicity with respect to intensity but only smoothness with respect to frequency for the audiometric function. This framework was initially evaluated in human subjects for threshold audiogram estimation. 2 repetitions of MLAG and 1 repetition of manual clinical audiometry were conducted in each of 21 participants. Results indicated that MLAG both agreed with clinical estimates and exhibited test-retest reliability to within accepted clinical standards, but with significantly fewer tone deliveries required compared to clinical methods while also providing an effectively continuous threshold estimate along frequency. This framework’s ability to evaluate full psychometric functions was then evaluated using simulated experiments. As a feasibility check, performance for estimating unidimensional psychometric functions was assessed and directly compared to inference using standard maximum-likelihood probit regression; results indicated that the two methods exhibited near identical performance for estimating threshold and spread. MLAG was then used to estimate 2-dimensional audiometric functions constructed using existing audiogram phenotypes. Results showed that this framework could estimate both threshold and spread of the full audiometric function with high accuracy and reliability given a sufficient sample count; non-active sampling using the Halton set required between 50-100 queries to reach clinical reliability, while active sampling strategies reduced the required number to around 20-30, with Bayesian active leaning by disagreement exhibiting the best performance of the tested methods. Overall, MLAG’s accuracy, reliability, and high degree of detail make it a promising method for estimation of threshold audiograms and audiometric functions, and the framework’s flexibility enables it to be easily extended to other psychophysical domains

Definizione, studio e progetto preliminare di una tecnica di geo-localizzazione di sorgenti interferenti per satelliti commerciali di telecomunicazioni

L’argomento del dottorato riguarda le telecomunicazioni satellitari commerciali. In particolare tratta della possibilità di poter definire, progettare e valutare mediante analisi e simulazioni, un sistema in grado di geo-localizzare sorgenti interferenti nell’area di copertura dell’antenna a bordo satellite per telecomunicazioni (area di servizio). Tale soluzione tecnologica rappresenta un valido supporto per intervenire a seguito di uno o più eventi interferenti. Tale intervento può essere o di tipo passivo, quanto il satellite non è provvisto di sotto-sistema di contromisura, oppure attivo quando il satellite è provvisto a bordo di sistema di contromisura