Multiple Model Filtering for Failure Identification in Large Space Structures

Future space structures, while obeying the low-mass-to-orbit requirement, are expected to drastically increase their size. As redundancy in structural elements is typically not allowed, the risk of failures increases, and information about damaged parts could be useful to avoid excess loads. Furthermore, as distributed active control is likely to be included, new schemes could be envisaged to bear with detected failure(s). Identification of weakened elements in these large structures is indeed of paramount importance. Bayesian estimators, intrinsically capable to work with noisy data provided by sensors, can be a suitable tool for monitoring the health of structure. The paper investigates the performance of these estimators for failure detection and identification problems referring to a rich, realistic model of a future large radar satellite. Multiple models built on different possible behaviors are considered together to timely set the alarm when the likelihood threshold for a possible failure is passed. The capability to identify differently located occurrences is analyzed, discussing the confidence in the solution. Aside from the well-known literature on Bayesian estimators, focus is on the hints which could be gained from realistic simulations in view of the possible operational applications to space structures

A self-organizing state space model and simplex initial distribution search

Self-organizing state space model, Monte Carlo particle filter, Parameter estimation, Simplex Nelder-Mead algorithm,

Data fusion and tracking with multiple UAVs

This chapter describes decentralized data fusion algorithms for a team of multiple autonomous platforms. Decentralized data fusion (DDF) provides a useful basis with which to build upon for cooperative information gathering tasks for robotic teams operating in outdoor environments. Through the DDF algorithms, each platform can maintain a consistent global solution from which decisions may then be made. Comparisons will be made between the implementation of DDF using two probabilistic representations. The first, Gaussian estimates and the second Gaussian mixtures are compared using a common data set. The overall system design is detailed, providing insight into the overall complexity of implementing a robust DDF system for use in information gathering tasks in outdoor UAV applications

A Robust Approach for Clock Offset Estimation in Wireless Sensor Networks

The maximum likelihood estimators (MLEs) for the clock phase offset assuming a two-way message exchange mechanism between the nodes of a wireless sensor network were recently derived assuming Gaussian and exponential network delays. However, the MLE performs poorly in the presence of non-Gaussian or nonexponential network delay distributions. Currently, there is a need to develop clock synchronization algorithms that are robust to the distribution of network delays. This paper proposes a clock offset estimator based on the composite particle filter (CPF) to cope with the possible asymmetries and non-Gaussianity of the network delay distributions. Also, a variant of the CPF approach based on the bootstrap sampling (BS) is shown to exhibit good performance in the presence of reduced number of observations. Computer simulations illustrate that the basic CPF and its BS-based variant present superior performance than MLE under general random network delay distributions such as asymmetric Gaussian, exponential, Gamma, Weibull as well as various mixtures