Ieee Eascon Proceedings
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International Reference Guide to Space Launch Systems
This best-selling reference guide contains the most reliable and up-to-date material on launch programs in Brazil, China, Europe, India, Israel, Japan, Russia, Ukraine, and the United States. Packed with illustrations and figures, the third edition has been extensively updated and expanded, and offers a quick and easy data retrieval source for policymakers, planners, engineers, launch buyers, and students.
Advanced Kalman Filtering, Least-Squares and Modeling
This book is intended primarily as a handbook for engineers who must design practical systems. Its primary goal is to discuss model development in sufficient detail so that the reader may design an estimator that meets all application requirements and is robust to modeling assumptions. Since it is sometimes difficult to a priori determine the best model structure, use of exploratory data analysis to define model structure is discussed. Methods for deciding on the “best” model are also presented. A second goal is to present little known extensions of least squares estimation or Kalman filtering that provide guidance on model structure and parameters, or make the estimator more robust to changes in real-world behavior. A third goal is discussion of implementation issues that make the estimator more accurate or efficient, or that make it flexible so that model alternatives can be easily compared. The fourth goal is to provide the designer/analyst with guidance in evaluating estimator performance and in determining/correcting problems. The final goal is to provide a subroutine library that simplifies implementation, and flexible general purpose high-level drivers that allow both easy analysis of alternative models and access to extensions of the basic filtering. Supplemental materials and up-to-date errata are downloadable at http://booksupport.wiley.com.
A Technique for Coherent Detection and Relating of Spectral Signals
An important class of spectral signals is that for which the components within a signal set are characterized by complex emvelopes which exhibit correlation. A technique is proposed for extraction of such a signal set based on an orthogonal decomposition of the cross (discrete) frequency correlation matrix describing the set in the presence of additive uncorrelated noise. Certain advantages of the technique relative to those based of Fourier analysis with either smoothed estimated power spectrum or minimum variance processing are presented. Specifically, it is illustrated how the detection performance of the technique is established by the total energy in the spectral set rather than by the levels of individual spectral elements in the set. Furthermore, it is shown how minimum variance spectrum analysis actually can suppress components in such a set. Finally, simulated examples are presented which illustrate the performance of the technique. (Author).