A range-only multiple target particle filter tracker
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In this paper, we propose a particle filter acoustic direction-of-arrival (DOA) tracker to track multiple maneuvering targets using a state space approach. The particle filter determines its state vector using a batch of DOA estimates. The filter likelihoo ...
Institute of Electrical and Electronics Engineers2005
We propose a particle filter acoustic tracker to track multiple maneuvering targets using a state space formulation with a locally linear motion model. The observations are a batch of direction-of-arrival (DOA) estimates at various frequencies. The data li ...
A proposal function determines the random particle support of a particle filter. When this support is distributed close to the true target density, filter's estimation performance increases for a given number of particles. In this paper, a proposal strateg ...
In time series analysis state-space models provide a wide and flexible class. The basic idea is to describe an unobservable phenomenon of interest on the basis of noisy data. The first constituent of such a model is the so-called state equation, which char ...
Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...
In this paper, a novel particle filter tracker is presented for target tracking using collocated radar and acoustic sensors. Real-time tracking of the target's position and velocity in Cartesian coordinates is performed using batches of range and direction ...
This paper presents a Rao-Blackwellized mixed state particle filter for joint head tracking and pose estimation. Rao-Blackwellizing a particle filter consists of marginalizing some of the variables of the state space in order to exactly compute their poste ...
Particle filters are now established as the most popular method for visual tracking. Within this framework, it is generally assumed that the data are temporally independent given the sequence of object states. In this paper, we argue that in general the da ...
We introduce a new method for approximate inference in Hybrid Dynamical Graphical models, in particular, for switching dynamical networks. For the important special case of switching linear Gaussian state space models (switching Kalman Filters), our method ...
This paper presents a Rao-Blackwellized mixed state particle filter for joint head tracking and pose estimation. Rao-Blackwellizing a particle filter consists of marginalizing some of the variables of the state space in order to exactly compute their poste ...
