The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association pr...

We study online approximations to Gaussian process models for spatially distributed systems. We apply our method to the prediction of wind fields over the ocean surface from scatterometer data. Our approach combines a sequential update of a Gaussian approximation to the posterior with a sparse representation that allows to treat problems with a large number of observations.

In this paper we introduce the idea of probability in the definition of Sequential Dynamical Systems, thus obtaining a new concept, Probabilistic Sequential System. The introduction of a probabilistic structure on Sequential Dynamical Systems is an important and interesting problem. The notion of homomorphism of our new model, is a natural extension of homomorphism of sequential dynamical syste...

We consider simulation-based methods for exploration and maximization of expected utility in sequential decision problems. We consider problems which require backward induction with analytically intractable expected utility integrals at each stage. We propose to use forward simulation to approximate the integral expressions, and a reduction of the allowable action space to avoid problems relate...

Posterior Cramér Rao lower bounds (PCRLBs) [1] for sequential Bayesian estimators provide performance bounds for general nonlinear filtering problems and have been used widely for sensor management in tracking and fusion systems. However, the unconditional PCRLB [1] is an off-line bound that is obtained by taking the expectation of the Fisher information matrix (FIM) with respect to the measure...

Flexible and low-complexity variable rate coding schemes based on rate-compatible convolutional codes are presented. The codes have a wide range of code rates and are optimized for good performance on both AWGN and Rayleigh fading channels. Furthermore, the application of these codes for rate matching, combined coding and spreading in a DS-CDMA system and hybrid type-II ARQ schemes are demonstr...

Flexible and low-complexity variable rate coding schemes based on rate-compatible convolutional codes are presented. The codes have a wide range of code rates and are optimized for good performance on both AWGN and Rayleigh fading channels. Furthermore, the application of these codes for rate matching, combined coding and spreading in a DS-CDMA system and hybrid type-II ARQ schemes are demonstr...

Abstract In the mid-1980s, still in his young 40s, André Journel was already recognized as one of giants geostatistics. Many contributions from new research program at Stanford University had centered around indicator methods that he developed: kriging and multiple kriging. But when second crop graduate students arrived Stanford, lacked an approach to conditional simulation not tainted by what ...

We propose a novel strategy for training neural networks using sequential sampling-importance resampling algorithms. This global optimisation strategy allows us to learn the probability distribution of the network weights in a sequential framework. It is well suited to applications involving on-line, nonlinear, non-Gaussian or non-stationary signal processing.

Within MUCM there might occasionally arise the need to use large training set sizes, or employ observations with non-Gaussian noise characteristics or non-linear sensor models in a calibration stage. This technical report deals with Gaussian process models in these non-Gaussian, and / or large data set size cases. Treating such data within Gaussian processes is most naturally accomplished using...