In modern systems for air surveillance, it is important to have a high quality situation assessment. SAAB has a system for air surveillance, and in this thesis possible improvements of the tracking performance of this system are explored. The focus has been on improving the tracking of highly maneuverable targets observed with low sampling rate. To evaluate improvements of the tracking performa...

Sequential Monte Carlo techniques are useful for state estimation in non-linear, non-Gaussian dynamic models. These methods allow us to approximate the joint posterior distribution using sequential importance sampling. In this framework, the dimension of the target distribution grows with each time step, thus it is necessary to introduce some resampling steps to ensure that the estimates provid...

This paper addresses optimal estimation for time varying autoregressive (TVAR) models. First, we propose a statistical model on the time evolution of the frequencies, moduli and real poles instead of a standard model on the AR coefficients as it makes more sense from a physical viewpoint. Second, optimal estimation involves solving a complex optimal filtering problem which does not admit any cl...

Abstract. In this paper we cast the idea of antithetic sampling, widely used in standard Monte Carlo simulation, into the framework of sequential Monte Carlo methods. A version of the standard auxiliary particle filter (Pitt and Shephard, 1999) is proposed where the particles are mutated blockwise in such a way that all particles within each block are, firstly, offspring of a common ancestor an...

In this paper we propose a sequential Monte Carlo algorithm to estimate a stochastic volatility model with leverage effect, non constant conditional mean and jumps. Our idea relies on the auxiliary particle filter algorithm together with the Markov Chain Monte Carlo (MCMC) methodology. Our method allows to sequentially evaluate the parameters and the latent processes involved in the dynamic of ...

Stochastic volatility models are important tools for studying the behavior of many financial markets. For this reason a number of versions have been introduced and studied in the recent literature. The goal is to review and compare some of these alternatives by using Bayesian procedures. The quantity used to assess the goodness-of-fit is the Bayes factor, whereas the ability to forecast the vol...

Sequential Monte Carlo is a useful simulation-based method for online filtering of state-space models. For certain complex state-space models, a single proposal distribution is usually not satisfactory and using multiple proposal distributions is a general approach to address various aspects of the filtering problem. This article proposes an efficient method of using multiple proposals in combi...

In this paper we present an efficient particle filtering method to perform optimal estimation in Jump Markov (Nonlinear) Systems (JMS). Such processes consist of a mixture of heterogeneous models and possess a natural hierarchical structure. We take advantage of these specificities in order to develop a generic filtering methodology for these models. The method relies on an original and non tri...

In this article we study asymptotic properties of weighted samples produced by the auxiliary particle filter (APF) proposed by Pitt and Shephard (1999a). Besides establishing a central limit theorem (CLT) for smoothed particle estimates, we also derive bounds on the L p error and bias of the same for a finite particle sample size. By examining the recursive formula for the asymptotic variance o...

This paper compares several particle filtering variants for speech feature enhancement in non-stationary noise environments. By analyzing the random processes of clean speech, noise and noisy speech, appropriate proposal densities are derived. The performances of the resulting particle filters, i.e. modified Sampling-ImportanceResampling (mod-SIR), auxiliary SIR and likelihood particle filter, ...