16 accepted papers
We consider the classical detection problem, of deciding whether a received signal consists of noise only or of a known signal of interest embedded in noise. The classical tool used for such problems is the Matched Filter (MF), which, under the assumption of Gaussian noise, provides the optimal deci…
We consider the blind separation of noisy mixtures of independent sources over a finite field. Namely, the source signals, the elements of the mixing matrix, the noise signals, the noisy output signals and the associated arithmetic operations all reside in a finite (Galois) field. The source signals…
Probability mass function (PMF) estimation using a low-rank model for the PMF tensor has gained increased popularity in recent years. However, its performance evaluation relied mostly on empirical testing. In this work, we derive theoretical bounds on the attainable performance under this model assu…
We consider the blind estimation / equalization of a Moving Average (MA) channel over a finite field. In this framework, the channel’s input and output signals, as well as its coefficients, belong to a finite (Galois) field, and all summation and multiplication operations are calculated modulo the f…
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 pr…
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" spars…
We study the blind calibration problem of uniform linear arrays of acoustic vector sensors for narrowband Gaussian signals, and propose an improved, asymptotically optimal blind calibration scheme. Following recent work by Ramamohan et al., we exploit the special (block-Toeplitz) structure of the un…
We address the problem of source separation from noisy mixtures in a semi-blind scenario, with stationary, temporally-diverse Gaussian sources and known spectra. In such noisy models, a dilemma arises regarding the desired objective. On one hand, a "maximally separating" solution, providing the mini…
Tensor decompositions are regarded as a powerful tool for multidimensional signal processing. In this contribution, we focus on the well-known Canonical Polyadic (CP) decomposition and present a first-order perturbation analysis of the SEmi-algebraic framework for approximate CP decompositions via S…
Electrocardiography (ECG) measured using wearable wireless sensors is already commonly used for several years, as one of the products of the emerging Telemedicine field, which is one the main branches in eHealth applications. In this work we address the problem of missing samples recovery of such EC…
In its basic, fully blind form, Independent Component Analysis (ICA) does not rely on a particular statistical model of the sources, but only on their mutual statistical independence, and therefore does not admit a Maximum Likelihood (ML) estimation framework. In semi-blind scenarios statistical mod…
The “Sequentially Drilled” Joint Congruence (SeDJoCo) transformation is a set of matrix transformation equations, which coincide with the Likelihood Equations for semi-blind source separation, when each source is modeled as a zero-mean Gaussian process with a known (and distinct) temporal covariance…
Joint EigenValue Decomposition (JEVD) algorithms are widely used in many application scenarios. These algorithms can be divided into different categories based on the cost function that needs to be minimized. Most of the frequently used algorithms in the literature use indirect least square (LS) cri…
This paper presents a new perspective of beamforming designs in Coordinated Multi-Point (CoMP) downlink systems that are combined with multicast schemes. The beamformer computation is expressed as a joint matrix transformation that can be regarded as an extension of the "Sequentially Drilled" Joint…
In the context of Maximum Likelihood (ML) source separation in a semi-blind scenario, where the spectra of the sources are known and distinct, the likelihood equations amount to a set of matrix decompositions (known as the "Sequentially Drilled" Joint Congruence Transformation (SeDJoCo)). However, q…
We consider self-localization in an ad-hoc, asynchronous sensors network. A mobile beacon transmits a short wideband signal from a few locations, unknown to the sensors. Each of the sensors receives the transmissions and estimates their Times of Arrival (TOAs) relative to its own timebase, which has…