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The multi-Bernoulli (MB) filter for extended targets has been derived recently. However, the implementation of the extended target (ET) MB filter for nonlinear non-Gaussian models has not been presented. In this paper, we propose the sequential Monte Carlo (SMC) implementation of the ET-MB filter for estimating multiple extended targets by using the SMC technique and measurement partitioning algorithm...
In this paper, we design a learning drift homotopy particle filter algorithm. We employ the drift homotopy technique in the extra Markov Chain Monte Carlo move after the resampling step of the generic particle filter algorithm to efficiently resolve the degeneracy of the algorithm. In this work, we use the effective sample size as a learning parameter to control the levels of drift homotopy which...
We propose a new method to estimate the trajectory of a source based on the two angles of the arrival lines of two waves emitted by the source but propagating with two different speeds. This difference between speeds is due to either the different natures of the waves or the different natures of the media in which the waves propagate. The source is assumed to move with a constant velocity and the...
Medical literature have recognized physical activity as a key factor for a healthy life due to its remarkable benefits. However, there is a great variety of physical activities and not all of them have the same effects on health nor require the same effort. As a result, and due to the ubiquity of commodity devices able to track users' motion, there is an increasing interest on performing activity...
We propose the use of multivariate version of Whittle's methodology to estimate periodic autoregressive moving average models. In the literature, this estimator has been widely used to deal with large data sets, since, in this context, its performance is similar to the Gaussian maximum likelihood estimator and the estimates are obtained much faster. Here, the usefulness of Whittle estimator is illustrated...
This paper presents an effective method to estimate the permittivity profile from the scattered field measured outside scatterers by minimizing the non-radiating objective function using Monte Carlo approach. Since the non-radiating objective function is inherently non-linear as reported in [1], it is necessary to use some random perturbation to prevent the permittivity profile estimation from getting...
Parallelizability of an algorithm is nowadays a highly desirable property as computer hardware is becoming increasingly parallel. In this paper, a formulation of the particle filtering algorithm, suitable for parallel or distributed computing, is proposed. From the particle set, a series expansion is fitted to the posterior probability density function. The global information provided by the particles...
Since lithium-ion batteries have been used in a wide range of fields, such as transportation industry, household appliances, and national defence industry. In order to avoid the unnecessary loss resulting from its sudden failure, it is necessary to timely predict the remaining useful life (RUL) of lithium-ion battery. In this paper, we present a novel remaining useful life estimation method for lithium-ion...
This paper presents a new Bayesian collaborative sparse regression method for linear unmixing of hyperspectral images. Our contribution is twofold; first, we propose a new Bayesian model for structured sparse regression in which the supports of the sparse abundance vectors are a priori spatially correlated across pixels. Secondly, we propose an advanced Markov chain Monte Carlo algorithm to estimate...
The estimation of the impulse response of a linear dynamic system is of crucial importance in many measurement problems. When the task of collecting a large amount of measurements represents an expensive and time-consuming procedure, an accurate estimate needs to be extracted based on a short input/output data record. Well-tuned regularization methods are getting popular to improve the impulse response...
The use of normal approximation to estimate expanded uncertainty has been very widespread; yet this is one of the practices that is being criticized by various quarters for lack of rigor and potentially misleading. Monte Carlo method is probably the only method trusted to generate reliable expanded uncertainty. Unfortunately, Monte Carlo method is not applicable for type-A evaluations. This is one...
The performances of sixteen equation error methods for continuous-time system identification are compared through a simulation example with the CONTSID toolbox. The influence of the sampling period, the type of input signal (piece-wise constant or band-limited) and the noise (level and type: white/colored) is studied. The methods are then classed according to quantitative and qualitative criteria.
In this paper, we present the application of particle filtering to build efficient equalizer structures for mobile station terminals. We begin by recalling the theory of particle filtering and we put the emphasis on the key point of such a technique, the sampling importance resampling (SIR) or the sequential importance sampling (SIS). Then, we present a mathematical model which allows to represent...
In this article, we address the problem of Bayesian de-convolution of point sources with Poisson statistics. A high level Bayesian approach is proposed to solve this problem. The original image is modeled list of an unknown number of points sources with unknow parameters. A prior distribution reflecting our degree of belief is introduced on all these unknown parameters, including the number of sources...
In this paper we address the problem of the Bayesian de-convolution of a widely spread class of processes, filtered point processes, whose underlying point process is a self-excited point process. In order to achieve this de-convolution, we perform powerful stochastic algorithm, the Markov chains Monte Carlo (MCMC), which despite their power have not been yet widely used in signal processing.
This paper considers the application of ΜΛ cumulant enhancement to the identification of the parameters of a causal nonminimum phase ARMA(p, q) system which is excited by an unobservable independent identically distributed (IID) non-Gaussian process. The method proposed in this paper is based on the double MA method of [l]. The cumulant enhancement is used to improve the cumulante of the two intermediate...
We discuss the application of signal parameter estimators for periodic point process signals with missing data. The proposed estimation techniques operate on the observed event arrival time sequence of a pulse train signal and have application to pulse train signal classification and signal reconstruction. The methods we describe are based on the use of circular statistics and are shown to offer considerable...
The paper deals with Gibbs samplers that include high-dimensional conditional Gaussian distributions. It proposes an efficient algorithm that only requires a scalar Gaussian sampling. The algorithm relies on a random excursion along a random direction. It is proved to converge, i.e. the drawn samples are asymptotically under the target distribution. Our original motivation is in unsupervised inverse...
This paper proposes a new Bayesian strategy for the estimation of smooth parameters from nonlinear models. The observed signal is assumed to be corrupted by an independent and non identically (colored) Gaussian distribution. A prior enforcing a smooth temporal evolution of the model parameters is considered. The joint posterior distribution of the unknown parameter vector is then derived. A Gibbs...
In the last decade alpha-stable distributions have become a standard model for impulsive data. Especially the linear symmetric alpha-stable processes have found applications in various fields. When the process parameters are time-invariant, various techniques are available for estimation. However, time-invariance is an important restriction given that in many communications applications channels are...
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