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The difficulty of image blind restoration is the lack of sufficient information of the point spread function to cause the ill-posed problem. In order to achieve satisfactory results of image restoration, and at the same time to speed up the restoration, a Bayesian image blind restoration method based on differential evolution optimization is proposed. Firstly, the Gauss model and Laplace model are...
We present a system suitable for human multi-robot interaction that supports the operator in the robot selection process. The proposed framework allows a human to issue commands to a robotic team without an explicit robot selection in so enabling a fluent interaction. This work is framed in the operative context of the SHERPA project [1], which proposes the deployment of a robotic platform for Search...
The detection of heartbeat is an important and challenging issue for health care. This work proposes to estimate the QRS complex parameters based on the maximum-likelihood (ML) principle. To this goal, a new signal model and its Bayesian framework are studied. Detectors or estimators based on the Bayesian framework are considered to be optimal in the statistical signal processing point of view. To...
In this paper we describe a particle filter algorithm that allows incorporation of prior knowledge about future states. Incorporation of such knowledge can significantly reduce the uncertainty in the estimation of future state predictions. Estimation of the state is based on a transition model where the current state is not only conditioned on the previous state but also on an attractive potential...
This paper surveys some recent results regarding the Cramér-Rao Lower Bound (CRLB) — the requirements for its existence and its extension to the situation where the parameter's likelihood function (LF) support depends on the parameter to be estimated. In the latter case the conventional CRLB does not hold in general. First we revisit the derivation of the CRLB to elucidate the necessary and sufficient...
The optimal non-linear filter estimates can be obtained by solving the Fokker-Planck equation (FPE) for the time propagation, together with the Bayesian measurement inclusion. An issue faced when solving the FPE is the curse of dimensionality. Recently, a tensor based approach has been proposed, which is said to be suitable for high dimensional problems. Then Bayesian measurement inclusion also presents...
Physical systems switching between various working regimes are often encountered in practical applications. However, transition probabilities, according to which a system switches from the current regime to another one, are commonly designed as a priori known parameters, and their misspecification can degrade the performance of the algorithms filtering (or estimating) latent variables of the system...
We propose a novel progressive Gaussian filter for nonlinear stochastic systems. A Gaussian approximation of the posterior is computed without an explicit assumption of a linear relation between the system state and the measurement. This allows for better quality of the estimation compared to Kalman filters for nonlinear problems like the EKF or UKF. In this work, we use the progressive filter framework,...
In this paper, we consider clutter estimation issue under unknown, non-uniform and time-varying clutter background. First, we use finite mixture distributions (FMD) to fit the unknown clutter. As for the parameters of the FMD, we adopt the Gibbs sampler and Bayesian information criterion (BIC) to derive and evaluate clutter parameters. The final experiments show that the proposed algorithm can effectively...
This paper studies the problem of simultaneous deciding on hypotheses and estimating a random parameter. We propose a joint decision and estimation (JDE) formulation, which amounts to minimizing a risk related to both decision and estimation while decision performance is also constrained within a tolerable level. The risk used in this paper is a weighted sum of estimation costs conditioned on correct...
Multipath is one of the most penalizing error sources in GPS navigation. It occurs when the satellite signals are reflected on obstacles before reaching the GPS receiver, corrupting the satellite-receiver distance measurements. In recent works, Bayesian non parametric (BNP) models of the measurement errors in the presence of multipath were considered. The latter were assumed to be distributed according...
We address analytic solutions of the Weiss-Weinstein bound (WWB), which lower bounds the mean squared error of Bayesian inferrers. The bound supports discrete, absolutely continuous, and singular continuous probability distributions, the latter corresponding to joint estimation and detection. We present new analytical solutions for truncated Gaussian, Laplace, categorical, uniform, and Bernoulli distributions...
Control loop performance monitoring and diagnosis is an important practical topic in process industries. Data-driven Bayesian method is attracting more and more research attention, which uses multiple monitors to yield probabilistic assessments. However, the correlation among variables, high-dimensional observations, and discrete degree of the evidence affects the diagnostic performance. This paper...
The directions of arrival (DOA) of plane waves are estimated from multi-frequency multi-snapshot sensor array data using Sparse Bayesian Learning (SBL). The prior for the source amplitudes is assumed to be independently zero-mean complex Gaussian distributed with hyperparameters being the unknown variances (i.e. the source powers). For a complex Gaussian likelihood with unknown noise variance hyperparameter,...
This paper presents a distributed Bayesian filtering (DBF) method for a network of multiple unmanned ground vehicles (UGVs) under dynamically changing interaction topologies. The information exchange among UGVs relies on a measurement dissemination scheme, called Latest-In-and-Full-Out (LIFO) protocol. Different from statistics dissemination approaches that transmit posterior distributions or likelihood...
In several high-resolution array processing applications such as radar and sonar, it is necessary to localize targets that have a finite angular spread. In such scenarios, conventional subspace-based techniques tend to provide erroneous results, and hence, an extended target model is more appropriate. In this work, we consider a multiple input multiple output system model and propose to jointly estimate...
Robust estimation is an important and timely research subject. In this paper, we investigate performance lower bounds on the mean-square-error (MSE) of any estimator for the Bayesian linear model, corrupted by a noise distributed according to an i.i.d. Student's t-distribution. This class of prior parametrized by its degree of freedom is relevant to modelize either dense or sparse (accounting for...
This paper is focused on probabilistic estimation for the attitude dynamics of a rigid body on the special orthogonal group. We select the matrix Fisher distribution to represent the uncertainties of attitude estimates and measurements in a global fashion without need for local coordinates. Several properties of the matrix Fisher distribution on the special orthogonal group are presented, and an unscented...
We investigate the performance of some sparse recovery and compressed sensing algorithms when applied to the Angle-of-Arrival (AoA) estimation problem. In particular, we review three different approaches in compressed sensing, namely Pursuit-type, Thresholding-type, and Bayesian-based algorithms. The compressed sensing algorithms reviewed herein are of vast interest when applied to AoA estimation...
We address the problem of estimating the visual focus of attention (VFOA), e.g. who is looking at whom? This is of particular interest in human-robot interactive scenarios, e.g. when the task requires to identify targets of interest over time. The paper makes the following contributions. We propose a Bayesian temporal model that connects VFOA to gaze direction and to head pose. Model inference is...
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