• Information fusion

• Multiple Target Tracking

• Particle Filtering

• Track before Detection

• Infrared Image Processing and Dim Target Detection

Particle Filtering

        Sequential Monte Carlo (SMC) methods are a set of flexible simulation-based methods for sampling from a sequence of probability distributions; each distribution being only known up to a normalising constant. These methods were originally introduced in the early 50's by physicists and have become very popular over the past few years in statistics and related fields. For example, they are now extensively used to solve sequential Bayesian inference problems arising in econometrics, advanced signal processing or robotics.

        SMC methods approximate the sequence of probability distributions of interest using a large set of random samples, named particles. These particles are propagated over time using simple Importance Sampling (IS) and resampling mechanisms. Asymptotically, i.e. as the number of particles goes to infinity, the convergence of these particle approximations towards the sequence of probability distributions can be ensured under very weak assumptions. However, for practical implementations, a finite and sometimes quite restricted number of particles has to be considered. Much research is therefore devoted to the design of efficient sampling strategies in order to sample particles in regions of high probability mass.