More rain compensation results

To reduce the impact of rain-induced attenuation in the 20/30 GHz band, the attenuation at a specified signal frequency must be estimated and extrapolated forward in time on the basis of a noisy beacon measurement. Several studies have used model based procedures for solving this problem in statistical inference. Perhaps the most widely used model-based paradigm leads to the Kalman filter and its lineal variants. In this formulation, the dynamic features of the attenuation are represented by a state process (x(sub t)). The observation process (y(sub t)) is derived from beacon measurements. Some ideas relating to the signal processing problems related to uplink power control are presented. It is shown that some easily implemented algorithms hold promise for use in estimating rain induced fades. The algorithms were applied to actual data generated at the Virginia Polytechnic Institute and State University (VPI) test facility. Because only one such event was studied, it is not clear that the algorithms will have the same effectiveness when a wide range of events are studied

A non-autonomous stochastic discrete time system with uniform disturbances

The main objective of this article is to present Bayesian optimal control over a class of non-autonomous linear stochastic discrete time systems with disturbances belonging to a family of the one parameter uniform distributions. It is proved that the Bayes control for the Pareto priors is the solution of a linear system of algebraic equations. For the case that this linear system is singular, we apply optimization techniques to gain the Bayesian optimal control. These results are extended to generalized linear stochastic systems of difference equations and provide the Bayesian optimal control for the case where the coefficients of these type of systems are non-square matrices. The paper extends the results of the authors developed for system with disturbances belonging to the exponential family

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This highly interdisciplinary project extends previous work in combat modeling and in control-theoretic descriptions of decision-making human factors in complex activities. A previous paper has established the first theory of the statistical mechanics of combat (SMC), developed using modern methods of statistical mechanics, baselined to empirical data gleaned from the National Training Center (NTC). This previous project has also established a JANUS(T)-NTC computer simulation/wargame of NTC, providing a statistical ‘‘what-if ’’ capability for NTC scenarios. This mathematical formulation is ripe for control-theoretic extension to include human factors, a methodology previously developed in the context of teleoperated vehicles. Similar NTC scenarios differing at crucial decision points will be used for data to model the influence of decision making on combat. The results may then be used to improve present human factors and C 2 algorithms in computer simulations/wargames. Our approach is to ‘‘subordinate’ ’ the SMC nonlinear stochastic equations, fitted to NTC scenarios, to establish the zeroth order description of that combat. In practice, an equivalent mathematical-physics representation is used, more suitable for numerical and formal work, i.e., a Lagrangian representation. Theoretically, these equations are nested within a larger set of nonlinear stochastic operator-equations which include C 3 human factors, e.g., supervisory decisions. In this study, we propose to perturb this operator theory about the SMC zeroth order set of equations. Then, subsets of scenarios fit to zeroth order, originally considered to be similarly degenerate, can be further split perturbatively to distinguish C 3 decision-making influences. New methods of Very Fast Simulated Re-Annealing (VFSR), developed in the previous project, will be used for fitting these models to empirical data

Locating, classifying and countering agile land vehicles: with applications to command architectures

This book examines real-time target tracking and identification algorithms with a focus on tracking an agile target. The authors look at several problems in which the tradeoff of accuracy and confidence must be made. These issues are explored within the context of specific tracking scenarios chosen to illustrate the tradeoffs in a simple and direct manner. The text covers the Gaussian wavelet estimator (GWE) which has a flexible architecture that is able to fuse uncommon sensor combinations with non-temporal structural constraints.  ·         Discusses applied estimation and prediction of terrestrial targets ·         Covers fusion of heterogeneous sensor types and tracking with non-temporal engagement constraints ·         Examines confidence that the target will be captured and motion analysis of land vehicles

and

This highly interdisciplinary project extends previous work in combat modeling and in control-theoretic descriptions of decision-making human factors in complex activities. A previous paper has established the first theory of the statistical mechanics of combat (SMC), developed using modern methods of statistical mechanics, baselined to empirical data gleaned from the National Training Center (NTC). This previous project has also established aJANUS(T)-NTC computer simulation/wargame of NTC, providing a statistical ‘‘what-if ’’ capability for NTC scenarios. This mathematical formulation is ripe for control-theoretic extension to include human factors, a methodology previously developed in the context of teleoperated vehicles. Similar NTC scenarios differing at crucial decision points will be used for data to model the influence of decision making on combat. The results may then be used to improve present human factors and C 2 algorithms in computer simulations/wargames. Our approach is to ‘‘subordinate’ ’ the SMC nonlinear stochastic equations, fitted to NTC scenarios, to establish the zeroth order description of that combat. In practice, an equivalent mathematical-physics representation is used, more suitable for numerical and formal work, i.e., a Lagrangian representation. Theoretically, these equations are nested within a larger set of nonlinear stochastic operator-equations which include C 3 human factors, e.g., supervisory decisions. In this study, wepropose to perturb this operator theory about the SMC zeroth order set of equations. Then, subsets of scenarios fit to zeroth order, originally considered to be similarly degenerate, can be further split perturbatively to distinguish C 3 decision-making influences. New methods of Very Fast Simulated Re-Annealing (VFSR), developed in the previous project, will be used for fitting these models to empirical data

Receivers for multi-mode channels

© Copyright 2003 IEEEConsiderable work has been done on receiver design for slowly varying, random communication channels. This paper employs a hybrid estimation approach to develop a receiver that is useful when the channel is more volatile. An example illustrates the technique when the channel has a "white " component