Particle and cell approximations for nonlinear filtering
Abstract
We consider the nonlinear filtering problem for systems with noise--free state equation. First, we study a particle approximation of the a posteriori probability distribution, and we give an estimate of the approximation error. Then we show, and we illustrate with numerical examples, that this approximation can produce a non consistent estimation of the state of the system when the measurement noise tends to zero. Hence, we propose a histogram--like modification of the particle approximation, which is always consistent. Finally, we present an application to target motion analysis.
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