Errors-In-Variables-Based Approach for the Identification of AR Time-Varying Fading Channels

This letter deals with the identification of time-varying Rayleigh fading channels using a training sequence-based approach. When the fading channel is approximated by an autoregressive (AR) process, it can be estimated by means of Kalman filtering, for instance. However, this method requires the estimations of both the AR parameters and the noise variances in the state–space representation of the system. For this purpose, the existing noise compensated approaches could be considered, but they usually require a long observation window and do not necessarily provide reliable estimates when the signal-to-noise ratio is low. Therefore, we propose to view the channel identification as an errors-in-variables (EIV) issue. The method consists in searching the noise variances that enable specific noise compensated autocorrelation matrices of observations to be positive semidefinite. In addition, the AR parameters can be estimated from the null spaces of these matrices. Simulation results confirm the effectiveness of this approach, especially in presence of a high amount of noise

Long-term X-ray variability of Swift J1644+57

We studied the X-ray timing and spectral variability of the X-ray source Sw J1644+57, a candidate for a tidal disruption event. We have separated the long-term trend (an initial decline followed by a plateau) from the short-term dips in the Swift light-curve. Power spectra and Lomb-Scargle periodograms hint at possible periodic modulation. By using structure function analysis, we have shown that the dips were not random but occurred preferentially at time intervals ~ [2.3, 4.5, 9] x 10^5 s and their higher-order multiples. After the plateau epoch, dipping resumed at ~ [0.7, 1.4] x 10^6 s and their multiples. We have also found that the X-ray spectrum became much softer during each of the early dip, while the spectrum outside the dips became mildly harder in its long-term evolution. We propose that the jet in the system undergoes precession and nutation, which causes the collimated core of the jet briefly to go out of our line of sight. The combined effects of precession and nutation provide a natural explanation for the peculiar patterns of the dips. We interpret the slow hardening of the baseline flux as a transition from an extended, optically thin emission region to a compact, more opaque emission core at the base of the jet.Comment: 16 pages, 12 figures. Accepted by MNRAS on 2012 Feb 11; minor improvements in the introduction and discussion from the previous arXiv versio

XIPE: the x-ray imaging polarimetry explorer

XIPE, the X-ray Imaging Polarimetry Explorer, is a mission dedicated to X-ray Astronomy. At the time of writing XIPE is in a competitive phase A as fourth medium size mission of ESA (M4). It promises to reopen the polarimetry window in high energy Astrophysics after more than 4 decades thanks to a detector that efficiently exploits the photoelectric effect and to X-ray optics with large effective area. XIPE uniqueness is time-spectrally-spatially- resolved X-ray polarimetry as a breakthrough in high energy astrophysics and fundamental physics. Indeed the payload consists of three Gas Pixel Detectors at the focus of three X-ray optics with a total effective area larger than one XMM mirror but with a low weight. The payload is compatible with the fairing of the Vega launcher. XIPE is designed as an observatory for X-ray astronomers with 75 % of the time dedicated to a Guest Observer competitive program and it is organized as a consortium across Europe with main contributions from Italy, Germany, Spain, United Kingdom, Poland, Sweden

Structural health monitoring application of errors-in-variables identification

Structural Health Monitoring denotes a set of methodologies oriented to the description of the dynamical behavior of a structure in view of damage detection. These methodologies have taken advantage from the development of sensor, modeling and network techniques and constitute, today, a well established area. One of the most used methods consists in deducing dynamic models from the observations and in comparing these models with reference ones, concerning integrity conditions of the monitored structure. In many cases the excitations can be considered as White noise in the range of frequencies of interest and, in these cases, the structure can be described by means of autoregressive models. When this approximation is not realistic it is necessary to use input/output models that take into account also the characteristics of the excitation. This last case is considered in this paper making reference to the use of Errors\u2013in\u2013Variables (EIV) models and to data collected on a real structure during a small seismic event

Fast filtering of noisy autoregressive signals

Autoregressive (AR) models are used in a wide variety of applications concerning the recovery of signals from noise-corrupted observations. In all real contexts of this kind also an additive broadband observation noise is present and the filtering of the observations is usually performed by means of standard Kalman filtering that requires a state space realization of the AR model to describe the observed process and the solution, at every step, of the Riccati equation. This paper proposes a faster filtering algorithm suitable for stationary processes and based on the decomposition of Toeplitz matrices described in (Rissanen, Mathematics of Computation, vol. 27, pp. 147-154, 1973) that operates directly on AR models. The computational complexity of the proposed algorithm increases only linearly with the order of the process

The Frisch scheme in multivariable errors-in-variables identification

This paper concerns the identification of multivariable errors-in-variables (EIV) models, i.e. models where all inputs and outputs are assumed as affected by additive errors. The identification of MIMO EIV models introduces challenges not present in SISO and MISO cases. The approach proposed in the paper is based on the extension of the dynamic Frisch scheme to the MIMO case. In particular, the described identification procedure relies on the association of EIV models with directions in the noise space and on the properties of a set of high order Yule\ue2\u80\u93Walker equations. A method for estimating the system structure is also described

On the use of minimal parametrizations in multivariable output-error identification

Multivariable output–error identification does not constitute, in any way, a straightforward extension of the scalar case. The aim of this paper is twofold: 1) Introduction of a new minimal parametrization for multivariable output error models leading to an easily implementable prediction error identification procedure; 2) Comparison of PEM and errors–in–variables approaches based on the dynamic Frisch scheme in the identification of MIMO output error processes