Talks and Poster Presentations (with Proceedings-Entry):

O. Hlinka, F. Hlawatsch:
"Time-Space-Sequential Algorithms For Distributed Bayesian State Estimation In Serial Sensor Networks";
Talk: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), Taipei, Taiwan; 04-19-2009 - 04-24-2009; in: "Proc. IEEE ICASSP 2009", IEEE, (2009), ISBN: 978-1-4244-2354-5; 2057 - 2060.

English abstract:
We consider distributed estimation of a time-dependent, random
state vector based on a generally nonlinear/non-Gaussian state-space
model. The current state is sensed by a serial sensor network without
a fusion center. We present an optimal distributed Bayesian estimation
algorithm that is sequential both in time and in space (i.e.,
across sensors) and requires only local communication between
neighboring sensors. For the linear/Gaussian case, the algorithm
reduces to a time-space-sequential, distributed form of the Kalman
filter. We also demonstrate the application of our state estimator to
a target tracking problem, using a dynamically defined "local sensor
chain" around the current target position.

Parameter estimation, state estimation, sequential Bayesian filtering, distributed inference, sensor networks, Kalman filter, target tracking

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.