We present a novel idea for modeling maneuvering targets using GARCH process noise. GARCH processes seem to capture well the desired properties of evading aerial target maneuvers and, hence, overcome the shortcomings ...
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ISBN:
(纸本)9781627481144
We present a novel idea for modeling maneuvering targets using GARCH process noise. GARCH processes seem to capture well the desired properties of evading aerial target maneuvers and, hence, overcome the shortcomings of classical modeling methods. We derive two recursive state estimation algorithms for systems following the new model. The first method is based on a heuristic utilization of the KF mechanization with an ad-hoc technique for process noise estimation. The second algorithm consists of a direct utilization of the EKF approach. Being only an initial attempt, both filters succeed, to some extent, to estimate the target acceleration using position-only measurements.
This work considers the economic aspects in control implementation of second order spatially distributed systems. By opting to use multiple actuator/sensor pairs with limited capacity, instead of a single actuator/sen...
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We revisit the problem of tracking the state of a hybrid system capable of performing a bounded number of mode switches. In a previous paper we have addressed a version of the problem where we have assumed the existen...
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ISBN:
(纸本)9781617824012
We revisit the problem of tracking the state of a hybrid system capable of performing a bounded number of mode switches. In a previous paper we have addressed a version of the problem where we have assumed the existence of a deterministic, known hard bound on the number of mode transitions. In addition, it was assumed that the system can possess only two modes, e.g., the maneuvering and non-maneuvering regimes of a tracked target. In the present paper we relax both assumptions: we assume a soft, stochastic bound on the number of mode transitions, and altogether remove the restriction on the number of modes of the system (thus, e.g., the target can have multiple different maneuvering modes, in addition to the non-maneuvering one). While admitting an unlimited number of mode transitions, the soft bound renders that number finite with probability 1. In addition, similarly to the case where the number of transition was deterministically hard-bounded, the existence of the bound renders the mode switching mechanism non-Markov. Thus, the two formulations address similar, though not identical, problems, that cannot be solved by direct application of algorithms devised for hybrid systems having Markov mode switching mechanisms. The novel solution approach adopted herein is based on transforming the non-Markovian mode switching mechanism to an equivalent Markovian one, at the price of augmenting the mode definition, and increasing the dimension of the state space involved. A standard interacting multiple model (IMM) filter is then applied to the transformed (Markovian) problem in a straightforward manner. The performance of the new method is demonstrated via a simulation study comprising three examples, in which the new method is compared with 1) the filter for hard-bounded mode transitions, and 2) a standard IMM filter directly applied to the original problem. The study shows that even when working outside its operating envelope (e.g., when the number of mode switches is har
The key issues in CO2 sequestration monitoring involve accurate monitoring, from the injection stage to prediction & verification, of CO2 movement over time for environmental considerations. A natural non-intrusiv...
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Compressive sensing (CS) is a new approach to simultaneous sensing and compression for sparse and compressible signals. While the discrete Fourier transform has been widely used for CS of frequency-sparse signals, it ...
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Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in groups of independent agents who accumulate evidence t...
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Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in groups of independent agents who accumulate evidence the first to decide are those with the strongest initial biases. Their decisions align with their initial bias, regardless of the underlying truth. In contrast, agents who decide last make decisions as if they were initially unbiased and hence make better choices. We obtain asymptotic expressions in the large population limit quantifying how agents' initial inclinations shape early decisions. Our analysis shows how bias, information quality, and decision order interact in nontrivial ways to determine the reliability of decisions in a group.
3D structure recovery from a collection of 2D images requires the estimation of the camera locations and orientations, i.e. the camera motion. For large, irregular collections of images, existing methods for the locat...
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The dynamic behavior of RMSprop and Adam algorithms is studied through a combination of careful numerical experiments and theoretical explanations. Three types of qualitative features are observed in the training loss...
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New techniques are presented for the calculation of lower bounds to energies of atoms and ions. A combination of effective-field and intermediate-problem methods is used to seek intermediate operators which rapidly ap...
New techniques are presented for the calculation of lower bounds to energies of atoms and ions. A combination of effective-field and intermediate-problem methods is used to seek intermediate operators which rapidly approximate the lower spectrum of the full Hamiltonian. Effectiveness of the techniques is demonstrated by showing that very small-scale calculations suffice to free the ground state of Li from the continuum, and to approximate the Li+ ground state within 0.8%.
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