For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems. With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
According to the customary literary-historical and theoretical notion, the fact that the first modern novel represents a parody or travesty of the chivalric ideal merits no particular attention. Failing to become attuned to the real role of the chivalric ideal at the beginning of the era of the modern novel, commentators missed the chance to adequately review the role of chivalry at the end of that period. The modern novel did not only begin, but also ended with a travesty of the chivalric ideal. The deep need of a significant number of modernist writers to measure their own time according to the ideals of the high and late Middle Ages cannot, therefore, be explained by a set of literary-historical, spiritual-historical or social circumstances. The predilection of a range of twentieth century novelists for a distant feudal past suggests that there exists a fundamental poetic connection between the modern (or at least the modernist) novel and the ideals of chivalry.
The main objective of this book is to present the distribution and diversity of major soil types in Serbia. It focuses on giving a detailed description of the physical, chemical and biological properties of soil and their geomorphological forms, as well as the geological characteristics of parent material. An integrative approach is used to study the interaction between climate, vegetation and geology in soil formation. Special attention is paid to human-induced soil degradation due to the erosion and contamination of soils in Serbia. The book includes a harmonization of national soil classification systems, with the FAO, WBR and ESD systems.
This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
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