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.
Fault detection and isolation is crucial for efficient operation and safety of any industrial process. Methods from all the areas of data analysis are being used for this task including Bayesian reasoning and Kalman filtering. In this paper authors use the discrete Field Kalman Filter for detecting and recognising faulty conditions of the system. Proposed approach, devised for stochastic linear systems...
Most current state of the art blind image deconvolution methods model the underlying image (either in the image or filter space) using sparsity promoting priors and perform inference, that is, image, blur, and parameter estimation using variational approximation. In this paper we propose the use of the spike-and-slab prior model in the filter space and a variational posterior approximation more expressive...
In this paper, we investigate the recovery of range and spectral profiles associated with remote three-dimensional scenes sensed via single-photon multispectral Lidar (MSL). We consider different spatial/spectral sampling strategies and pare their performance for similar overall numbers of detected photons. For a regular spatial grid, the first strategy consists of sampling all the spatial locations...
In this paper a new direct nonparametric estimation of the period and the shape of a periodic component in short duration signals is proposed and evaluated. Classical Fourier Transform (FT) methods lack precision and resolution when the duration of the signal is very short and the signal is noisy. The proposed method is based on the direct description of the problem as a linear inverse problem and...
In linear/nonlinear dynamical systems, there are many situations where model parameters cannot be obtained a priori or vary with time. As a consequence, the estimation algorithms that are based on the exact knowledge of these model parameters cannot be accurate in this context. In this work, a kernel density-based particle filter is investigated to jointly estimate the states and unknown time-varying...
Hyperparameter estimation is a recurrent problem in the signal and statistics literature. Popular strategies are cross-validation or Bayesian inference, yet it remains an active topic of research in order to offer better or faster algorithms. The models considered here are sparse regression models with convex or non-convex group-Lasso-like penalties. Following the recent work of Pereyra et al. [1]...
In this paper, a new high-resolution ISAR Imaging method by using sparse subband measurements is developed. It requires no resampling the irregularly measurements onto a uniform frequency grid. Firstly, a one-dimensional waveform dictionary for LFM signal after dechirping is constructed, and the principle of dictionary fusion is illustrated. Then, the two-dimensional waveform fusion dictionary is...
This paper investigates measurement scheduling for linear quadratic Gaussian (LQG) control. The measurement collected via the local sensor is sent over the bandwidth-limited channel to the remote controller for regulating the system state. An event-based scheme is designed at the local scheduler to smartly choose the time for sending the data, so as to reduce the uncertainty of system state at the...
In this paper, we consider the nonlinear filtering by using information geometric approach. Under the principle of Bayesian, the filtering problem has been converted to Bayesian estimation. Based on the estimation conditional on the measurement, the posterior probability density functions (PDFs) have constructed a statistical manifold. With the information geometric approach, the nonlinear characteristic...
Among learning based hashing methods, supervised hashing seeks compact binary representation of the training data to preserve semantic similarities. Recent years have witnessed various problem formulations and optimization methods for supervised hashing. Most of them optimize a form of loss function with a regulization term, which can be viewed as a maximum a posterior (MAP) estimation of the hashing...
As an important part of aircraft systems, accurate grasp of aircraft structure's health condition has become increasingly important. Remaining life prediction is playing an important role in structure health condition assessment. As one of the most popular and effective forecast algorithm, particle filtering method was used to predict the remaining life of aircraft structure. In this paper, we first...
Tacking of testability growth test (TGT) is an assurance of the orderly development of TGT. This paper, a testability growth tracking model based on Expected Bayesian method which under entropy loss function is studied in view of the lack of existing testability growth model and the subjective tracking of testability index. Firstly, the multi-layer prior distribution of the fault detection rate (FDR)...
We present a novel approach to noise-blind deblurring, the problem of deblurring an image with known blur, but unknown noise level. We introduce an efficient and robust solution based on a Bayesian framework using a smooth generalization of the 0-1 loss. A novel bound allows the calculation of very high-dimensional integrals in closed form. It avoids the degeneracy of Maximum a-Posteriori (MAP) estimates...
Ultra-wideband (UWB) signal offers high time resolution and precise time of arrive (TOA) estimation. But it requires extremely high sampling rate analog to digital conversion (ADC). To handle this problem, Compressive sampling (CS) theory has been used in UWB system to localize objects at a sampling rate much below the Nyquist rate. In this paper, we utilize Bayesian Compressive Sensing (BCS) to UWB...
A novel approach for two-dimensional direction of arrival (2D-DOA) estimation based on separable observation model utilizing weighted L1-norm penalty and multitask Bayesian compressive sensing is proposed. Unlike previous DOA estimation methods based on the separable observation model, the proposed method has a better performance at the circumstance of low SNRs and small scale planar array. This approach...
In this paper, we investigate the Bayesian filtering problem for discrete nonlinear dynamical systems which contain random parameters. An augmented cubature Kalman filter (CKF) is developed to deal with the random parameters, where the state vector is enlarged by incorporating the random parameters. The corresponding number of cubature points is increased, so the augmented CKF method requires more...
This paper addresses the problem of joint detection and estimation fusion when sensor quantized data are correlated in the distributed system. The traditional methods to handle this joint problem tend to treat the detection and estimation tasks separately, which put more emphasis on the detection part but treat the estimation part sub-optimally. In this work, the joint detection and estimation fusion...
Bayesian filters are often used in statistical inference and consist of recursively alternating between two steps: prediction and correction. Most commonly the Gaussian distribution is used within the Bayes filtering framework, but other distributions, which could model better the nature of the estimated phenomenon like the von Mises-Fisher distribution on the unit sphere, have also been subject of...
Estimation of periodic quantities such as angles or phase values is a common problem. However, standard approaches, for example the Kalman filter and extensions thereof, have difficulties when estimating periodic quantities. To address this problem, circular filtering algorithms have been proposed but they are limited to just a single angle. In order to deal with multiple, possibly correlated angles,...
This paper proposes a novel method to estimate the global scale of a 3D reconstructed model within a Kalman filtering-based monocular SLAM algorithm. Our Bayesian framework integrates height priors over the detected objects belonging to a set of broad predefined classes, based on recent advances in fast generic object detection. Each observation is produced on single frames, so that we do not need...
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.