14,956 research outputs found
A particle filtering approach for joint detection/estimation of multipath effects on GPS measurements
Multipath propagation causes major impairments to Global
Positioning System (GPS) based navigation. Multipath results in biased GPS measurements, hence inaccurate position estimates. In this work, multipath effects are considered as abrupt changes affecting the navigation system. A multiple model formulation is proposed whereby the changes are represented by a discrete valued process. The detection of the errors induced by multipath is handled by a Rao-Blackwellized particle filter (RBPF). The RBPF estimates the indicator process jointly with the navigation states and multipath biases. The interest of this approach is its ability to integrate a priori constraints about the propagation environment. The detection is improved by using information from near future GPS measurements at the particle filter (PF) sampling step. A computationally modest delayed sampling is developed, which is based on a minimal duration assumption for multipath effects. Finally, the standard PF resampling stage is modified to include an hypothesis test based decision step
Mathematical control of complex systems
Copyright © 2013 ZidongWang et al.This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
A Framework for Robust Assimilation of Potentially Malign Third-Party Data, and its Statistical Meaning
This paper presents a model-based method for fusing data from multiple
sensors with a hypothesis-test-based component for rejecting potentially faulty
or otherwise malign data. Our framework is based on an extension of the classic
particle filter algorithm for real-time state estimation of uncertain systems
with nonlinear dynamics with partial and noisy observations. This extension,
based on classical statistical theories, utilizes statistical tests against the
system's observation model. We discuss the application of the two major
statistical testing frameworks, Fisherian significance testing and
Neyman-Pearsonian hypothesis testing, to the Monte Carlo and sensor fusion
settings. The Monte Carlo Neyman-Pearson test we develop is useful when one has
a reliable model of faulty data, while the Fisher one is applicable when one
may not have a model of faults, which may occur when dealing with third-party
data, like GNSS data of transportation system users. These statistical tests
can be combined with a particle filter to obtain a Monte Carlo state estimation
scheme that is robust to faulty or outlier data. We present a synthetic freeway
traffic state estimation problem where the filters are able to reject simulated
faulty GNSS measurements. The fault-model-free Fisher filter, while
underperforming the Neyman-Pearson one when the latter has an accurate fault
model, outperforms it when the assumed fault model is incorrect.Comment: IEEE Intelligent Transportation Systems Magazine, special issue on
GNSS-based positionin
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
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