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The problem of direction-of-arrival (DOA) estimation using partly calibrated arrays composed of multiple identically oriented subarrays is considered. The subarrays are assumed to possess the shift-invariance property which is exploited to develop a distributed search-free DOA estimation algorithm that is based on the generalized eigendecomposition (GED) of a pair of covariance matrices. We propose...
This paper considers the Kalman filtering problem where sensor signals are transmitted over an unreliable network and thus observation packets could be lost randomly. It has been proven by Sinopoli et al. that there is a critical observation arrival probability such that the expectation of the estimation error covariance is always finite and the equilibrium of the Modified Algebraic Riccati Equation...
We propose a distributed regression algorithm with the capability of automatically calibrating its parameters during its on-line functioning. The estimation procedure corresponds to a Regularization Network, i.e., the structural form of the estimator is a linear combination of basis functions which coefficients are computed by solving a linear system. The automatic tuning strategy instead constructs...
The principal component analysis (PCA) is a well-known technique to detect, isolate and estimate faults affecting a system. However, PCA identifies only linear structures in a given dataset. In this paper, we propose a new technique to estimate the fault affecting nonlinear systems, within the frame of kernel machines. To this end, the kernel methods are combined to the PCA, the so-called kernel PCA...
The estimation of the modes of a linear dynamical network (specifically, a network synchronization process) is considered. We study pole/eigenvalue estimation when the network dynamics is subject to impulsive stimulation at one location, and time-course measurements are taken at a subset of network locations. We develop several characterizations of mode estimation performance, as indicated by the...
This paper considers the problem of sensor faulttolerant control of a class of spatially distributed systems subject to discrete and delayed sensor-controller communication. An integrated approach that combines data-based fault identification and stability-based fault accommodation is developed on the basis of a suitable reduced-order model of the infinitedimensional system. A moving-horizon parameter...
Consider a set of agents implementing the discrete time consensus algorithm. At each time step, all agents also transmit their states to a central estimator that wishes to identify the underlying topology and eigenvalues of the network. It does so by using a nonlinear least squares (NLS) algorithm to identify the state evolution matrix used in the consensus algorithm. We present a mechanism to protect...
In this paper, a methodology for an affine quasilinear parameter varying (qLPV) model derivation is proposed. The nonlinear model of the system is converted into a qLPV model by hiding the nonlinearities in the scheduling parameters. In order to select the most suitable model among all the possible models, an algorithm is introduced and proposed to generate affine qLPV models for enhancing the fault...
In this paper, we examine data-driven aspects of consensus networks influenced by a stubborn agent. In particular we show that the judicious placement of the stubborn agent can be achieved based on snapshots of the data generated by the network through estimating the appropriate eigenvector of the perturbed Laplacian matrix. The exact dynamic mode decomposition algorithm is employed for estimating...
The second smallest eigenvalue equation of the Laplacian L of a network G is a parameter that captures important properties of the network. Applications such as synchronization of networked systems, consensus-based algorithms and network connectivity control may require one to regulate the magnitude of equation in order to achieve suitable network performance. The problem of decentralized estimation...
This paper presents a statistical modeling framework termed as PRISM for text-independent speaker verification. We decompose the verification task into three subtasks: PRobability density estimation, Information metric and Subspace/Manifold learning (PRISM). Subsequently, we take advantages of variational maximum likelihood estimation, Fisher information metric and discriminant locality preserving...
This paper analyses gait patterns of patients with Parkinson;s Disease (PD) based on the acceleration data given by an e-AR sensor. Ten PD patients wearing the e-AR sensor walked along a 7m walkway and each session contained 16 repeated trials. An iterative algorithm has been proposed to produce robust estimations in the case of measurement noise and short-duration of gait signals. Step-frequency...
Event-based sampling strategies allow for reducing the amount of communications between a sensor and the estimation module. This reduction is interesting specially when the sensors are linked by a shared wireless network. This paper proposes new event-based sampling methods based on the Mahalanobis distance concept. Combined with an Event-Based State Estimator, they provide the same level of performance...
In this paper a state estimator for high tech flexible systems with an inherent nonlinearity in the output dynamics is proposed. We consider an application in which sensor measurements of the flexible system become parameter (position) dependent. An LPV setting is proposed for the design of estimators that estimate flexible modes of the system. The possibility of pole placement for the error dynamics...
In this paper, basics of Direction of Arrival (DOA) estimation techniques were reviewed along with simulated results. It justifies the pros and cons of each technique. The purposes of using smart antenna system in this paper are regarded with the respect of DOA estimation, Path Delays estimation, and accurate channel estimation between a transmitter and an array of receivers. Considering the uplink...
This paper presents a geometrical method for solving the non-negative Blind Source Separation (BSS) problem. The method is based on a weak sparsity condition: for each source, there should exist at least one observed vector where only this source is non-zero. The method does not require but allows the sum-to-one constraint for the mixing parameters or sources. Considering each observed vector as an...
Reliable surface normal computation is fundamental for a broad range of computer vision application areas, e.g. object segmentation, classification and recognition. Naturally, the surface normal is computed on the acquired depth data, whereby the normal quality is dependent on noise performance and resolution of the underlying image modality. The tendency of combining different imaging sensors into...
In this paper, we consider the problem of connectivity maintenance in multi-robot systems with unicycle kinematics. While previous work has approached this problem through local control techniques, we propose a solution which achieves global connectivity maintenance under nonholonomic constraints. In addition, our formulation only requires intermittent estimation of algebraic connectivity, and accommodates...
This paper addresses the issue of Orthogonal Techniques for Blind Source Separation of periodic signals when the mixtures are corrupted with spatially correlated noises. The noise covariance matrix is assumed to be unknown. This problem is of major interest with experimental signals. We first remind that Principal Components Analysis (PCA) cannot provide a correct estimate of the signal subspace in...
In the spectral analysis of short data signals composed of sinusoids or exponentials, the source number estimation is a crucial problem for the performances of the high resolution methods (MUSIC, ESPRIT, etc.)· Indeed, for those methods, we must truncate the covariance matrix in signal and noise subspaces. That truncation depends on the estimation of the source number. So, we propose new criteria...
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