arXiv subjects

Alle-Jan van der Veen

Publications and source records attributed to Alle-Jan van der Veen.

At least 19 recordsLinked to original sources

arXiv2023

A Singular-value-based Marker for the Detection of Atrial Fibrillation Using High-resolution Electrograms and Multi-lead ECG

The severity of atrial fibrillation (AF) can be assessed from intra-operative epicardial measurements (high-resolution electrograms), using metrics such as conduction block (CB) and continuous conduction delay and block (cCDCB). These features capture differences in conduction velocity and wavefront propagation. However, they do not clearly differentiate patients with various degrees of AF while they are in sinus rhythm, and complementary features are needed. In this work, we focus on the morphology of the action potentials, and derive features to detect variations in the atrial potential waveforms. Methods: We show that the spatial variation of atrial potential morphology during a single beat may be described by changes in the singular values of the epicardial measurement matrix. The method is non-parametric and requires little preprocessing. A corresponding singular value map points at areas subject to fractionation and block. Further, we developed an experiment where we simultaneously measure electrograms (EGMs) and a multi-lead ECG. Results: The captured data showed that the normalized singular values of the heartbeats during AF are higher than during SR, and that this difference is more pronounced for the (non-invasive) ECG data than for the EGM data, if the electrodes are positioned at favorable locations. Conclusion: Overall, the singular value-based features are a useful indicator to detect and evaluate AF. Significance: The proposed method might be beneficial for identifying electropathological regions in the tissue without estimating the local activation time.

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arXiv2021

Analysis of multipath channel delay estimation using subspace fitting

The presence of rich scattering in indoor and urban radio propagation scenarios may cause a high arrival density of multipath components (MPCs). Often the MPCs arrive in clusters at the receiver, where MPCs within one cluster have similar angles and delays. The MPCs arriving within a single cluster are typically unresolvable in the delay domain. In this paper, we analyze the effects of unresolved MPCs on the bias of the delay estimation with a multiband subspace fitting algorithm. We treat the unresolved MPCs as a model error that results in perturbed subspace estimation. Starting from the first-order approximation of the perturbations, we derive the bias of the delay estimate of the line-of-sight (LOS) component. We show that it depends on the power and relative delay of the unresolved MPCs in the first cluster compared to the LOS component. Numerical experiments are included to show that the derived expression for the bias well describes the effects of unresolved MPCs on the delay estimation.

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arXiv2020

Joint blind calibration and time-delay estimation for multiband ranging

In this paper, we focus on the problem of blind joint calibration of multiband transceivers and time-delay (TD) estimation of multipath channels. We show that this problem can be formulated as a particular case of covariance matching. Although this problem is severely ill-posed, prior information about radio-frequency chain distortions and multipath channel sparsity is used for regularization. This approach leads to a biconvex optimization problem, which is formulated as a rank-constrained linear system and solved by a simple group Lasso algorithm.Numerical experiments show that the proposed algorithm provides better calibration and higher resolution for TD estimation than current state-of-the-art methods.

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arXiv2019

Time Delay Estimation from Multiband Radio Channel Samples in Nonuniform Noise

The multipath radio channel is considered to have a non-bandlimited channel impulse response. Therefore, it is challenging to achieve high resolution time-delay (TD) estimation of multipath components (MPCs) from bandlimited observations of communication signals. It this paper, we consider the problem of multiband channel sampling and TD estimation of MPCs. We assume that the nonideal multibranch receiver is used for multiband sampling, where the noise is nonuniform across the receiver branches. The resulting data model of Hankel matrices formed from acquired samples has multiple shift-invariance structures, and we propose an algorithm for TD estimation using weighted subspace fitting. The subspace fitting is formulated as a separable nonlinear least squares (NLS) problem, and it is solved using a variable projection method. The proposed algorithm supports high resolution TD estimation from an arbitrary number of bands, and it allows for nonuniform noise across the bands. Numerical simulations show that the algorithm almost attains the Cramer Rao Lower Bound, and it outperforms previously proposed methods such as multiresolution TOA, MI-MUSIC, and ESPRIT.

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arXiv2019

Multiresolution time-of-arrival estimation from multiband radio channel measurements

Achieving high resolution time-of-arrival (TOA) estimation in multipath propagation scenarios from bandlimited observations of communication signals is challenging because the multipath channel impulse response (CIR) is not bandlimited. Modeling the CIR as a sparse sequence of Diracs, TOA estimation becomes a problem of parametric spectral inference from observed bandlimited signals. To increase resolution without arriving at unrealistic sampling rates, we consider multiband sampling approach, and propose a practical multibranch receiver for the acquisition. The resulting data model exhibits multiple shift invariance structures, and we propose a corresponding multiresolution TOA estimation algorithm based on the ESPRIT algorithm. The performance of the algorithm is compared against the derived Cramér Rao Lower Bound, using simulations with standardized ultra-wideband (UWB) channel models. We show that the proposed approach provides high-resolution estimates while reducing spectral occupancy and sampling costs compared to traditional UWB approaches.

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arXiv2018

Joint Ranging and Clock Synchronization for Dense Heterogeneous IoT Networks

Synchronization and ranging in internet of things (IoT) networks are challenging due to the narrowband nature of signals used for communication between IoT nodes. Recently, several estimators for range estimation using phase difference of arrival (PDoA) measurements of narrowband signals have been proposed. However, these estimators are based on data models which do not consider the impact of clock-skew on the range estimation. In this paper, clock-skew and range estimation are studied under a unified framework. We derive a novel and precise data model for PDoA measurements which incorporates the unknown clock-skew effects. We then formulate joint estimation of the clock-skew and range as a two-dimensional (2-D) frequency estimation problem of a single complex sinusoid. Furthermore, we propose: (i) a two-way communication protocol for collecting PDoA measurements and (ii) a weighted least squares (WLS) algorithm for joint estimation of clock-skew and range leveraging the shift invariance property of the measurement data. Finally, through numerical experiments, the performance of the proposed protocol and estimator is compared against the Cramer Rao lower bound demonstrating that the proposed estimator is asymptotically efficient.

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arXiv2018

Relative kinematics of an anchorless network

Estimating the location of N coordinates in a P dimensional Euclidean space from pairwise distances (or proximity measurements), is a principal challenge in a wide variety of fields. Conventionally, when localizing a static network of immobile nodes, non-linear dimensional reduction techniques are applied on the measured Euclidean distance matrix (EDM) to obtain the relative coordinates upto a rotation and translation. In this article, we focus on an anchorless network of mobile nodes, where the distance measurements between the mobile nodes are time-varying in nature. Furthermore, in an anchorless network the absolute knowledge of any node positions, motion or reference frame is absent. We derive a novel data model which relates the time-varying EDMs to the time-varying relative positions of an anchorless network. Using this data model, we estimate the relative position, relative velocity and higher order derivatives, which are collectively termed as the relative kinematics of the anchorless network. The derived data model is inherently ill-posed, however can be solved using certain relative immobility constraints. We propose elegant closed form solutions to recursively estimate the relative kinematics of the network. For the sake of completeness, estimators are also proposed to find the absolute kinematics of the nodes, given known reference anchors. Cramer-Rao bounds are derived for the new data model and simulations are performed to analyze the performance of the proposed solutions.

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arXiv2018

Efficient Calibration of Radio Interferometers Using Block LDU Decomposition

Having an accurate calibration method is crucial for any scientific research done by a radio telescope. The next generation radio telescopes such as the Square Kilometre Array (SKA) will have a large number of receivers which will produce exabytes of data per day. In this paper we propose new direction-dependent and independent calibration algorithms that, while requiring much less storage during calibration, converge very fast. The calibration problem can be formulated as a non-linear least square optimization problem. We show that combining a block-LDU decomposition with Gauss-Newton iterations produces systems of equations with convergent matrices. This allows significant reduction in complexity per iteration and very fast converging algorithms. We also discuss extensions to direction-dependent calibration. The proposed algorithms are evaluated using simulations.

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arXiv2018

Constrained Least Squares for Extended Complex Factor Analysis

For subspace estimation with an unknown colored noise, Factor Analysis (FA) is a good candidate for replacing the popular eigenvalue decomposition (EVD). Finding the unknowns in factor analysis can be done by solving a non-linear least square problem. For this type of optimization problems, the Gauss-Newton (GN) algorithm is a powerful and simple method. The most expensive part of the GN algorithm is finding the direction of descent by solving a system of equations at each iteration. In this paper we show that for FA, the matrices involved in solving these systems of equations can be diagonalized in a closed form fashion and the solution can be found in a computationally efficient way. We show how the unknown parameters can be updated without actually constructing these matrices. The convergence performance of the algorithm is studied via numerical simulations.

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arXiv2015

Space-based Aperture Array For Ultra-Long Wavelength Radio Astronomy

The past decade has seen the rise of various radio astronomy arrays, particularly for low-frequency observations below 100MHz. These developments have been primarily driven by interesting and fundamental scientific questions, such as studying the dark ages and epoch of re-ionization, by detecting the highly red-shifted 21cm line emission. However, Earth-based radio astronomy below frequencies of 30MHz is severely restricted due to man-made interference, ionospheric distortion and almost complete non-transparency of the ionosphere below 10MHz. Therefore, this narrow spectral band remains possibly the last unexplored frequency range in radio astronomy. A straightforward solution to study the universe at these frequencies is to deploy a space-based antenna array far away from Earths' ionosphere. Various studies in the past were principally limited by technology and computing resources, however current processing and communication trends indicate otherwise. We briefly present the achievable science cases, and discuss the system design for selected scenarios, such as extra-galactic surveys. An extensive discussion is presented on various sub-systems of the potential satellite array, such as radio astronomical antenna design, the on-board signal processing, communication architectures and joint space-time estimation of the satellite network. In light of a scalable array and to avert single point of failure, we propose both centralized and distributed solutions for the ULW space-based array. We highlight the benefits of various deployment locations and summarize the technological challenges for future space-based radio arrays.

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arXiv2015

Radio Astronomical Image Formation using Constrained Least Squares and Krylov Subspaces

Image formation for radio astronomy can be defined as estimating the spatial power distribution of celestial sources over the sky, given an array of antennas. One of the challenges with image formation is that the problem becomes ill-posed as the number of pixels becomes large. The introduction of constraints that incorporate a-priori knowledge is crucial. In this paper we show that in addition to non-negativity, the magnitude of each pixel in an image is also bounded from above. Indeed, the classical "dirty image" is an upper bound, but a much tighter upper bound can be formed from the data using array processing techniques. This formulates image formation as a least squares optimization problem with inequality constraints. We propose to solve this constrained least squares problem using active set techniques, and the steps needed to implement it are described. It is shown that the least squares part of the problem can be efficiently implemented with Krylov subspace based techniques, where the structure of the problem allows massive parallelism and reduced storage needs. The performance of the algorithm is evaluated using simulations.

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arXiv2014

Joint ranging and synchronization for an anchorless network of mobile nodes

Synchronization and localization are critical challenges for the coherent functioning of a wireless network, which are conventionally solved independently. Recently, various estimators have been proposed for pairwise synchronization between immobile nodes, based on time stamp exchanges via two-way communication. In this paper, we consider a \textit{network of mobile nodes} for which a novel joint time-range model is presented, treating both unsynchronized clocks and the pairwise distances as a polynomial function of \textit{true} time. For a set of nodes, a pairwise least squares solution is proposed for estimating the pairwise range parameters between the nodes, in addition to estimating the clock offsets and clock skews. Extending these pairwise solutions to network-wide ranging and clock synchronization, we present a central data fusion based global least squares algorithm. A unique solution is non-existent without a constraint on the cost function (\eg clock reference node). Ergo, a constrained framework is proposed and a new Constrained \Cramer\ Rao Bound (CCRB) is derived for the joint time-range model. In addition, various constraints are proposed and their effects on the proposed algorithms are studied. Simulations are conducted and the proposed algorithm is shown to approach the theoretical limits.

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arXiv2014

Joint non-linear ranging and affine synchronization basis for a network of mobile nodes

Synchronization and localization are critical challenges for the coherent functioning of a wireless network of mobile nodes. In this paper, a novel joint non-linear range and affine time model is presented based on two way time stamp exchanges, extending an existing affine time-range model. For a pair of nodes, a closed form pairwise least squares solution is proposed for estimating pairwise range parameters, namely relative range, range rate and rate of range rate between the nodes, in addition to estimating the clock skews and the clock offsets. Extending these pair wise solutions to network wide ranging and clock synchronization, we present a central data fusion based global least squares solution. Furthermore, a new Constrained Cramer Rao Bound (CCRB) is derived for the joint time-range model and the proposed algorithms are shown to approach the theoretical limits.

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arXiv2014

Joint relative position and velocity estimation for an anchorless network of mobile nodes

Localization is a fundamental challenge for any wireless network of nodes, in particular when the nodes are mobile. We present an extension of the classical Multidimensional scaling (MDS) for an anchorless network of mobile nodes, wherein the solutions to the time-varying relative node positions are shown to lie in the derivatives of the time-varying inter-nodal pairwise distances. Moreover, we show that the relative position of a mobile node at each time instance is only dependent on the initial relative position, relative velocity and a common rotation matrix of the respective node, which are estimated using MDS-like and least squares estimators. Simulations are conducted to evaluate the performance of the proposed solutions and the results are presented.

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arXiv2013

60 GHz Wireless Link Within Metal Enclosures: Channel Measurements and System Analysis

Wireless channel measurement results for 60 GHz within a closed metal cabinet are provided. A metal cabinet is chosen to emulate the environment within a mechatronic system, which have metal enclosures in general. A frequency domain sounding technique is used to measure the wireless channel for different volumes of the metal enclosure, considering both line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Large-scale and small-scale characteristics of the wireless channel are extracted in order to build a comprehensive channel model. In contrast to conventional indoor channels at 60 GHz, the channel in the metal enclosure is highly reflective resulting in a rich scattering environment with a significantly large root-mean-square (RMS) delay spread. Based on the obtained measurement results, the bit error rate (BER) performance is evaluated for a wideband orthogonal frequency division multiplexing (OFDM) system.

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arXiv2013

Rigid Body Localization Using Sensor Networks: Position and Orientation Estimation

In this paper, we propose a novel framework called rigid body localization for joint position and orientation estimation of a rigid body. We consider a setup in which a few sensors are mounted on a rigid body. The absolute position of the sensors on the rigid body, or the absolute position of the rigid body itself is not known. However, we know how the sensors are mounted on the rigid body, i.e., the sensor topology is known. Using range-only measurements between the sensors and a few anchors (nodes with known absolute positions), and without using any inertial measurements (e.g., accelerometers), we estimate the position and orientation of the rigid body. For this purpose, the absolute position of the sensors is expressed as an affine function of the Stiefel manifold. In other words, we represent the orientation as a rotation matrix, and absolute position as a translation vector. We propose a least-squares (LS), simplified unitarily constrained LS (SUC-LS), and optimal unitarily constrained least-squares (OUC-LS) estimator, where the latter is based on Newton's method. As a benchmark, we derive a unitarily constrained Cramér-Rao bound (UC-CRB). The known topology of the sensors can sometimes be perturbed during fabrication. To take these perturbations into account, a simplified unitarily constrained total-least-squares (SUC-TLS), and an optimal unitarily constrained total-least-squares (OUC-TLS) estimator are also proposed.

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arXiv2013

Position and Orientation Estimation of a Rigid Body: Rigid Body Localization

Rigid body localization refers to a problem of estimating the position of a rigid body along with its orientation using anchors. We consider a setup in which a few sensors are mounted on a rigid body. The absolute position of the rigid body is not known, but, the relative position of the sensors or the topology of the sensors on the rigid body is known. We express the absolute position of the sensors as an affine function of the Stiefel manifold and propose a simple least-squares (LS) estimator as well as a constrained total least-squares (CTLS) estimator to jointly estimate the orientation and the position of the rigid body. To account for the perturbations of the sensors, we also propose a constrained total least-squares (CTLS) estimator. Analytical closed-form solutions for the proposed estimators are provided. Simulations are used to corroborate and analyze the performance of the proposed estimators.

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arXiv2013

Sparsity-Exploiting Anchor Placement for Localization in Sensor Networks

We consider the anchor placement problem in localization based on one-way ranging, in which either the sensor or the anchors send the ranging signals. The number of anchors deployed over a geographical area is generally sparse, and we show that the anchor placement can be formulated as the design of a sparse selection vector. Interestingly, the case in which the anchors send the ranging signals, results in a joint ranging energy optimization and anchor placement problem. We make abstraction of the localization algorithm and instead use the Cramér-Rao lower bound (CRB) as the performance constraint. The anchor placement problem is formulated as an elegant convex optimization problem which can be solved efficiently.

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