The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Online (or recursive) estimation of fixed model parameters in general state-space models is a crucial but often difficult task. This paper is about likelihood-based point estimation, showing that an online EM (Expectation-Maximization) algorithm recently proposed for discrete hidden Markov models can be extended to more general settings, including non-linear non-Gaussian state-space models that necessitate...
In this article, we propose a SMC based method for estimating the static parameter of a general state space model. The proposed method is based on maximizing the joint likelihood of the observation and unknown state sequence with respect to both the unknown parameters and the unknown state sequence. This in turn, casts the problem into simultaneous estimations of state and parameter. We show the efficacy...
Combination schemes are gaining attention as an interesting way to improve adaptive filter performance. In this paper we pay attention to a particular convex combination scheme with nonlinear adaptation that has recently been shown to be universal -i.e., to perform at least as the best component filter- in steady-state; however, no theoretical model for the transient has been provided yet. By relying...
A framework for the robust assessment of phase synchrony between multichannel observations is introduced. This is achieved by using empirical mode decomposition (EMD), a data driven technique which decomposes nonlinear and nonstationary data into their oscillatory components (scales). In general, it is rarely possible to jointly process two or more channels due to the non-uniqueness of the decompositions...
The empirical mode decomposition (EMD) algorithm is a fully data-driven method which is used to perform an adaptive decomposition of nonlinear and nonstationary signals. It has been recently illustrated that its complex extensions can be used to carry out fusion of multiple images. This is possible because the complex EMD allows comparison between common frequency scales, by aligning them within a...
Particle filters have become very popular signal processing tools for problems that involve nonlinear tracking of an unobserved signal of interest given a series of related observations. In this paper we propose a new scheme for particle filtering when the observed data are possibly contaminated with outliers. An outlier is an observation that has been generated by some (unknown) mechanism different...
In this paper, we present a sparse spatial spectrum estimation method which provides superresolution direction-finding performance and accurate signal power estimation simultaneously. Compressive sensing ideas are used in conjunction with a postulated model for the covariance matrix of the array output instead of the output itself. Following a preliminary analysis of the method and a geometric interpretation...
There have been several recently presented works on finding information-geometric embeddings using the properties of statistical manifolds. These methods have generally focused on embedding probability density functions into an open Euclidean space. In this paper we propose adding an additional constraint by embedding onto the surface of the sphere in an unsupervised manner. This additional constraint...
Contrary to the suboptimal (two-step) geolocation procedures, we propose a maximum likelihood estimation for the position of a stationary transmitter which its delayed and Doppler shifted signal is observed by moving receivers. The position is estimated based on the same data used in common methods. However, it is performed in a single step by maximizing a cost function that depends on the unknown...
Recently, a technique named `blind decorrelation' was proposed by which we can blindly diagonalize correlation matrices of isotropic noises observed by particular crystal-shape sensor arrays. This technique enables us to estimate the power of unknown target signals, which may improve the performance of inverse filters such as the Wiener filter. It was clarified that several classes of crystal-shape...
The problem of estimating the intrinsic dimensionality of certain point clouds is of interest in many applications in statistics and analysis of high-dimensional data sets. Our setting is the following: the points are sampled from a manifold M of dimension k, embedded in RopfD, with k Lt D, and corrupted by D-dimensional noise. When M is a linear manifold (hyperplane), one may analyse this situation...
This paper presents a linear high-order distributed average consensus (DAC) algorithmfor wireless sensor networks. The average consensus property and convergence rate of the highorder DAC algorithm are analyzed. In particular, the convergence rate is determined by the spectral radius of a network topology dependent matrix. Numerical results indicate that this simple linear high-order DAC algorithm...
This paper reports preliminary results of steady-state movement related potential (ssMRP) classification using hidden Markov models (HMM). Published works on electroencephalogram (EEG) signal classification mainly need experimenter interventions to accurately define temporal boundaries between the resting and motor execution states for the classifier where for asynchronous brain computer interfacing...
In the unscented Kalman filter (UKF), the state vector is typically augmented with process and measurement noise in order to approximate the joint predictive distribution of state and observation. For that, the unscented transform is used. As its point selection mechanism changes the higher order moments between the random variables, statistical independence is not preserved. In this work, we show...
A multiple observations based censored relay scheme has been proposed for a wireless sensor network. The relays in this scheme observe signals transmitted from multiple sensors and transmit decisions to the fusion center only if the likelihood ratios of the observations exceeded a predefined set of thresholds. This scheme has been shown to outperform the single observation based relay scheme in terms...
An iterative subspace-based method, termed pilot-aided subarray iterative (PASI) technique, is proposed for estimating directions-of-arrival (DOAs) of correlated narrowband signals impinging at an array. With the use of pilot signals, DOA estimation for each source can be carried out in parallel; with the use of subarrays, correlated signals can be decorrelated by forward spatial smoothing. The inclusion...
When sampling a stochastic process, the reconstruction may suffer from the reconstruction may suffer from the aliasing of frequency components located outside the Nyquist band. This paper shows that using multiple non uniform sampling schemes allow one to estimate the amplitude and the frequency of any monochromatic wave subjected to aliasing.
We consider the problem of assigning a class label to the noisy output of a linear system, where clean feature examples are available for training. We design a robust classifier that operates on a linear estimate, with uncertainty modeled by a Gaussian distribution with parameters derived from the bias and covariance of a linear estimator. Class-conditional distributions are modeled locally as Gaussians...
We consider the problem of estimating the structural breaks in a long memory FARIMA process. The number m of break points as well as their locations, the order (p, d, q) and the parameters of each regime are assumed to be unknown. To estimate the unknown parameters, we propose two criteria based on the minimum description length (MDL) principle of Rissanen, namely a direct extension of MDL and an...
This paper considers approximations of marginalization sums that arise in Bayesian inference problems. Optimal approximations of such marginalization sums, using a fixed number of terms, are analyzed for a simple model. The model under study is motivated by recent studies of linear regression problems with sparse parameter vectors, and of the problem of discriminating signal-plus-noise samples from...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.