9,243 research outputs found
Localisation of mobile nodes in wireless networks with correlated in time measurement noise.
Wireless sensor networks are an inherent part of decision making, object tracking and location awareness systems. This work is focused on simultaneous localisation of mobile nodes based on received signal strength indicators (RSSIs) with correlated in time measurement noises. Two approaches to deal with the correlated measurement noises are proposed in the framework of auxiliary particle filtering: with a noise augmented state vector and the second approach implements noise decorrelation. The performance of the two proposed multi model auxiliary particle filters (MM AUX-PFs) is validated over simulated and real RSSIs and high localisation accuracy is demonstrated
State-Observation Sampling and the Econometrics of Learning Models
In nonlinear state-space models, sequential learning about the hidden state
can proceed by particle filtering when the density of the observation
conditional on the state is available analytically (e.g. Gordon et al., 1993).
This condition need not hold in complex environments, such as the
incomplete-information equilibrium models considered in financial economics. In
this paper, we make two contributions to the learning literature. First, we
introduce a new filtering method, the state-observation sampling (SOS) filter,
for general state-space models with intractable observation densities. Second,
we develop an indirect inference-based estimator for a large class of
incomplete-information economies. We demonstrate the good performance of these
techniques on an asset pricing model with investor learning applied to over 80
years of daily equity returns
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