For better inference of the population quantity of interest, ratio estimators are often recommended when certain auxiliary variables are available. Two types of ratio estimators, modified for adaptive cluster sampling via transformed population and initial intersection probability approaches, have been studied in Dryver and Chao (2007). Unfortunately, none of them are a function of a minimal su...

In this contribution, we propose an efficient collaborative strategy for online change detection, in a distributed sensor network. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communication bandwith. The observed systems are assumed to have each a finite set of states, including the abrupt change behavior. For each disc...

In this contribution, we propose an efficient collaborative strategy for online change detection, in a distributed sensor network. The collaborative strategy ensures the efficiency and the robustness of the data processing, while limiting the required communication bandwith. The observed system is assumed to have a finite set of states, including the abrupt change behavior. For each discrete st...

This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the c...

There are two generations of Gibbs sampling methods for semi-parametric models involving the Dirichlet process. The rst generation suuered from a severe drawback; namely that the locations of the clusters, or groups of parameters, could essentially become xed, moving only rarely. Two strategies that have been proposed to create the second generation of Gibbs samplers are integration and appendi...

Particle filtering is a sequential Monte Carlo method [3] that uses importance sampling to draw samples from probability distributions. In a particle filter the target state is represented by a point mass particle set that is propagated and updated using conditional probability representations of the motion model and measurement model. Methods that improve the sampling efficiency include [3] re...

This article considers the application of particle filtering to continuousdiscrete optimal filtering problems, where the system model is a stochastic differential equation, and noisy measurements of the system are obtained at discrete instances of time. It is shown how the Girsanov theorem can be used for evaluating the likelihood ratios needed in importance sampling. It is also shown how the m...

In an earlier contribution we proposed a particle filter for underwater (UW) navigation, and applied it to an experimental trajectory. Here we focus on performance improvements and analysis. First, the Cramér Rao lower bound (CRLB) along the experimental trajectory is computed, which is only slightly lower than the particle filter estimate after initial transients. Simple rule of thumbs for how...