11,356 research outputs found
The Hybrid Density Filter for Nonlinear Estimation based on Hybrid Conditional Density
In nonlinear Bayesian estimation it is generally inevitable to incorporate approximate descriptions of the exact estimation algorithm. There are two possible ways to involve approximations: Approximating the nonlinear stochastic system model or approximating the prior probability density function. The key idea of the introduced novel estimator called Hybrid Density Filter relies on approximating the nonlinear system, thus approximating conditional densities. These densities nonlinearly relate the current system state to the future system state at predictions or to potential measurements at measurement updates. A hybrid density consisting of both Dirac delta functions and Gaussian densities is used for an optimal approximation. This paper addresses the optimization problem for treating the conditional density approximation. Furthermore, efficient estimation algorithms are derived based upon the special structure of the hybrid density, which yield a Gaussian mixture representation of the system state\u27s density
Interacting Multiple Model-Feedback Particle Filter for Stochastic Hybrid Systems
In this paper, a novel feedback control-based particle filter algorithm for
the continuous-time stochastic hybrid system estimation problem is presented.
This particle filter is referred to as the interacting multiple model-feedback
particle filter (IMM-FPF), and is based on the recently developed feedback
particle filter. The IMM-FPF is comprised of a series of parallel FPFs, one for
each discrete mode, and an exact filter recursion for the mode association
probability. The proposed IMM-FPF represents a generalization of the
Kalmanfilter based IMM algorithm to the general nonlinear filtering problem.
The remarkable conclusion of this paper is that the IMM-FPF algorithm retains
the innovation error-based feedback structure even for the nonlinear problem.
The interaction/merging process is also handled via a control-based approach.
The theoretical results are illustrated with the aid of a numerical example
problem for a maneuvering target tracking application
Particle filtering in high-dimensional chaotic systems
We present an efficient particle filtering algorithm for multiscale systems,
that is adapted for simple atmospheric dynamics models which are inherently
chaotic. Particle filters represent the posterior conditional distribution of
the state variables by a collection of particles, which evolves and adapts
recursively as new information becomes available. The difference between the
estimated state and the true state of the system constitutes the error in
specifying or forecasting the state, which is amplified in chaotic systems that
have a number of positive Lyapunov exponents. The purpose of the present paper
is to show that the homogenization method developed in Imkeller et al. (2011),
which is applicable to high dimensional multi-scale filtering problems, along
with important sampling and control methods can be used as a basic and flexible
tool for the construction of the proposal density inherent in particle
filtering. Finally, we apply the general homogenized particle filtering
algorithm developed here to the Lorenz'96 atmospheric model that mimics
mid-latitude atmospheric dynamics with microscopic convective processes.Comment: 28 pages, 12 figure
Probabilistic Framework for Sensor Management
A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions
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