Amir Weiss

Amir Weiss
Massachusetts Institute of Technology | MIT · Department of Electrical Engineering and Computer Science

About

49
Publications
1,410
Reads
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132
Citations
Introduction
My main research areas are in statistical and digital signal processing, array processing and estimation theory.

Publications

Publications (49)
Preprint
Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e.g., using time-difference of arrivals). However, underwater acoustic DLOC methods require prior knowledge of the environment, and are computationally costly...
Article
We consider the problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizable non-linea...
Preprint
Underwater acoustic localization has traditionally been challenging due to the presence of unknown environmental structure and dynamic conditions. The problem is richer still when such structure includes occlusion, which causes the loss of line-of-sight (LOS) between the acoustic source and the receivers, on which many of the existing localization...
Preprint
In a growing number of applications, there is a need to digitize a (possibly high) number of correlated signals whose spectral characteristics are challenging for traditional analog-to-digital converters (ADCs). Examples, among others, include multiple-input multiple-output systems where the ADCs must acquire at once several signals at a very wide...
Article
Full-text available
Side-channel analysis (SCA) attacks constantly improve and evolve. Implementations are therefore designed to withstand strong SCA adversaries. Different side channels exhibit varying statistical characteristics of the sensed or exfiltrated leakage, as well as the embedding of different countermeasures. This makes it crucial to improve and adapt pre...
Preprint
In a growing number of applications, there is a need to digitize signals whose spectral characteristics are challenging for traditional Analog-to-Digital Converters (ADCs). Examples, among others, include systems where the ADC must acquire at once a very wide but sparsely and dynamically occupied bandwidth supporting diverse services, as well as sy...
Article
A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural low-rank 4-way tensor representation. Thus, rather than estimati...
Preprint
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing. While many methods have been proposed for "classical" array structures, such as uniform linear arrays, not as many are found in the context of the more "modern" sparse arrays. In this paper, we present a novel blind...
Preprint
One-bit quantization has recently become an attractive option for data acquisition in cutting edge applications, due to the increasing demand for low power and higher sampling rates. Subsequently, the rejuvenated one-bit array processing field is now receiving more attention, as "classical" array processing techniques are adapted / modified accordi...
Preprint
Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multi-carrier digital communication technique. In recent years the interest in optical OFDM has grown significantly, due to its spectral efficiency and inherent resilience to frequency-selective channels and to narrowband interference. For these reasons it is curren...
Preprint
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Sz\'ekely et al.'s distance correlation measure, we define the Sources' Dependency Criterion (SDC), from which our estimate arises. Unlike most previously proposed estimates, the SDC estimate exploits the full independence of the sources and noise, as...
Preprint
Maximum Likelihood (ML) estimation requires precise knowledge of the underlying statistical model. In Quasi ML (QML), a presumed model is used as a substitute to the (unknown) true model. In the context of Independent Vector Analysis (IVA), we consider the Gaussian QML Estimate (QMLE) of the demixing matrices set and present an (approximate) analys...
Preprint
An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and...
Preprint
Independent Vector Analysis (IVA) has emerged in recent years as an extension of Independent Component Analysis (ICA) into multiple sets of mixtures, where the source signals in each set are independent, but may depend on source signals in the other sets. In a semi-blind IVA (or ICA) framework, information regarding the probability distributions of...
Article
An asymptotically optimal blind calibration scheme of uniform linear arrays for narrowband Gaussian signals is proposed. Rather than taking the direct Maximum Likelihood (ML) approach for joint estimation of all the unknown model parameters, which leads to a multi-dimensional optimization problem with no closed-form solution, we revisit Paulraj and...
Conference Paper
Classical Blind Source Separation (BSS) methods rarely attain exact separation, even under noiseless conditions. In addition, they often rely on particular structural or statistical assumptions (e.g., mutual independence) regarding the sources. In this work we consider a (realistic) "twist" in the classical linear BSS plot, which, quite surprisingl...
Preprint
We consider the fundamental problem of robust deconvolution, and particularly the recovery of an unknown deterministic signal convolved with a known filter and corrupted by additive noise. We present a novel, non-iterative data-driven approach. Specifically, our algorithm works in the frequency-domain, where it tries to mimic the optimal unrealizab...
Preprint
A blind Direction-of-Arrivals (DOAs) estimate of narrowband signals for Acoustic Vector-Sensor (AVS) arrays is proposed. Building upon the special structure of the signal measured by an AVS, we show that the covariance matrix of all the received signals from the array admits a natural low-rank 4-way tensor representation. Thus, rather than estimati...
Article
In the context of Independent Component Analysis (ICA), noisy mixtures pose a dilemma regarding the desired objective. On one hand, a “maximally separating” solution, providing the minimal attainable Interference-to-Source-Ratio (ISR), would often suffer from significant residual noise. On the other hand, optimal Minimum Mean Square Error (MMSE) es...
Article
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed. Based on Szekely et al. 's distance correlation measure, we define the Sources' Dependency Criterion (SDC), from which our estimate arises. Unlike most previously proposed estimates, the SDC estimate exploits the full independence of the sources and noise, as w...
Preprint
In the context of Independent Component Analysis (ICA), noisy mixtures pose a dilemma regarding the desired objective. On one hand, a "maximally separating" solution, providing the minimal attainable Interference-to-Source-Ratio (ISR), would often suffer from significant residual noise. On the other hand, optimal Minimum Mean Square Error (MMSE) es...
Article
Maximum Likelihood (ML) estimation requires precise knowledge of the underlying statistical model. In Quasi ML (QML), a presumed model is used as a substitute to the (unknown) true model. In the context of Independent Vector Analysis (IVA), we consider the Gaussian QML Estimate (QMLE) of the demixing matrices set and present an (approximate) analys...
Article
In Gaussian measurement models the measurements are given by a known function of the unknown parameter vector, contaminated by additive zero-mean Gaussian noise. When the function is linear, the resulting Maximum Likelihood estimate (MLE) is well-known to be efficient (unbiased, with a mean square estimation error (MSE) matrix attaining the Cramor-...
Article
The Semi-Algebraic framework for the approximate Canonical Polyadic (CP) decomposition via SImultaneaous matrix diagonalization (SECSI) is an efficient tool for the computation of the CP decomposition. The SECSI framework reformulates the CP decomposition into a set of joint eigenvalue decomposition (JEVD) problems. Solving all JEVDs, we obtain mul...
Article
Independent Vector Analysis (IVA) has emerged in recent years as an extension of Independent Component Analysis (ICA) into multiple sets of mixtures, where the source signals in each set are independent, but may depend on source signals in the other sets. In a semi-blind IVA (or ICA) framework, information regarding the probability distributions of...
Article
Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multicarrier digital communication technique. In recent years, the interest in optical OFDM has grown significantly, due to its spectral efficiency and inherent resilience to frequency-selective channels and to narrowband interference. For these reasons, it is curre...
Conference Paper
The Sequentially Drilled Joint Congruence (SeDJoCo) transformation has been identified as an important tool in the context of both Blind Source Separation (BSS) and closed-form Coordinated Beamforming (CBF) for the multi-user MIMO downlink. It can be interpreted as a new tensor decomposition. In this contribution, we introduce an extension of the S...
Conference Paper
Unlike linear analog channels, for which a significant number of well-studied, mostly simple equalization methods exist, non-linear analog channels pose a much more challenging task regarding the channel equalization, be it by means of analog and / or of digital operations. In this work we present a method for digital pre-distortion compensation fo...

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Projects (2)
Project
We address the problem of blind calibration, namely without using a known, user-controlled signal. Our novel approach yields enhanced solutions, which exploit statistical information encapsulated in correlations among the samples acquired from all sensors. In particular, we jointly estimate the sensors' gains and phases offsets, unlike previously proposed methods.