Related papers: Multisource Self-calibration for Sensor Arrays
Analytical expressions for covariances of weak lensing statistics related to the aperture mass $\Map$ are derived for realistic survey geometries such as SNAP for a range of smoothing angles and redshift bins. We incorporate the…
An iteratively reweighted least squares (IRLS) method is proposed for estimating polyserial and polychoric correlation coefficients in this paper. It iteratively calculates the slopes in a series of weighted linear regression models fitting…
We address the problem of estimating direction-of-arrivals (DOAs) for multiple acoustic sources in a reverberant environment using a spherical microphone array. It is well-known that multi-source DOA estimation is challenging in the…
With their ability to handle an increased amount of information, multivariate and multichannel signals can be used to solve problems normally not solvable with signals obtained from a single source. One such problem is the decomposition…
Weakly-supervised semantic segmentation (WSSS) performs pixel-wise classification given only image-level labels for training. Despite the difficulty of this task, the research community has achieved promising results over the last five…
In this paper, two novel algorithms to estimate a Gaussian Vector Autoregressive (VAR) model from 1-bit measurements are introduced. They are based on the Yule-Walker scheme modified to account for quantisation. The scalar case has been…
Nodes localization in Wireless Sensor Networks (WSN) has arisen as a very challenging problem in the research community. Most of the applications for WSN are not useful without a priori known nodes positions. One solution to the problem is…
In a companion paper we presented the theory for an antenna pattern measuring technique that uses (rather than mitigates) the properties of a multipath environment. Here we use measurements in a typical home garage to experimentally…
This paper studies sensor calibration in spectral estimation where the true frequencies are located on a continuous domain. We consider a uniform array of sensors that collects measurements whose spectrum is composed of a finite number of…
Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during learning, some methods have explored different penalty…
We propose an eigenvector-based formalism for the calibration of radio interferometer arrays. In the presence of a strong dominant point source, the complex gains of the array can be obtained by taking the first eigenvector of the…
The paper studies the scenario of wireless multicast with a single transmitter and a relay that deliver scalable source symbols to the receivers in a decode-and-forward (DF) fashion. With the end-to-end mean square error distortion (EED) as…
A weighted regression procedure is proposed for regression type problems where the innovations are heavy-tailed. This method approximates the least absolute regression method in large samples, and the main advantage will be if the sample is…
This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy…
Most existing antenna array-based source localization methods rely on fixed-position arrays (FPAs) and strict assumptions about source field conditions (near-field or far-field), which limits their effectiveness in complex, dynamic…
Calibration is a widely used method in survey sampling to adjust weights so that estimated totals of some chosen calibration variables match known population totals or totals obtained from other sources. When a large number of auxiliary…
This paper presents a novel sequential estimator for the direction-of-arrival and polynomial coefficients of wideband polynomial-phase signals impinging on a sensor array. Addressing the computational challenges of Maximum-likelihood…
Precise sensor calibration is critical for autonomous vehicles as a prerequisite for perception algorithms to function properly. Rotation error of one degree can translate to position error of meters in target object detection at large…
Two new methods are proposed for linear regression analysis for data with measurement errors. Both methods are designed to accommodate intrinsic scatter in addition to measurement errors. The first (BCES) is a direct extension of the…
We develop a framework for localized source detection in dynamical systems governed by nonlinear partial differential equations based on first and second-order sensitivity analysis. Building on the standard adjoint formulation, which…