K. V. Umamaheswara Reddy

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The design of an effective data assimilation environment for dispersion models is studied. These models are usually described by partial differential equations which lead to large scale state space models. The linear Kalman filter theory fails to meet the requirements of this application due to high dimensionality, strong non-linearities, non-Gaussian(More)
Data assimilation in the context of puff based dispersion models is studied. A representative two dimensional Gaussian puff atmospheric dispersion model is used for the purpose of testing and comparing several data assimilation techniques. A continuous nonlinear observation model, and a quantized probabilistic nonlinear observation model, are used to(More)
This article provides a suboptimal approach to the measurement update of the state vector and the associated state error covariance in the data assimilation process of airborne material dispersion systems, in which the state vector consists of Gaussian puffs and the sensor measurements of the local material concentrations are bar readings. Based on the(More)
This paper studies the problem of concurrent design of a feedback controller and a pre-filter to minimize a weighted cost comprising the maneuver time and the input power. The pre-filter is parameterized as a time-delay filter motivated by the Posicast controller. The proposed technique is illustrated on a double integrator assuming that the feedback(More)
Introduction: Mathematical models are approximate representations of physical processes and consequently have uncertainties associated with them. Furthermore, no sensor is perfect. Sensor measurements are generally some linear/nonlinear combination of states and are usually corrupted with quantization errors, superimposed noise, etc. Propagating the states(More)
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