Contributions to Books:

A.H. Sayed, P. Djuric, F. Hlawatsch:
"Distributed Kalman and Particle Filtering";
in: "Cooperative and Graph Signal Processing: Principles and Applications", P. Djuric, C. Richard (ed.); Academic Press / Elsevier, London, UK, 2018, (invited), ISBN: 9780128136775, 169 - 207.

English abstract:
This chapter discusses distributed Kalman and particle filtering algorithms for state estimation in decentralized multi-agent networks. It is assumed that the spatially distributed agents acquire local measurements with information about a time-varying state described by some underlying state-space model. The agents seek to estimate the time-varying state in a decentralized manner. They are only allowed to interact locally by sharing data or estimates with their immediate neighbors. It is shown how the agents can construct local estimates of the state trajectory through a cooperative process of interactions. Both diffusion- and consensus-based strategies are presented.

distributed sequential estimation, distributed Kalman filtering, distributed particle filtering, diffusion, distributed proposal adaptation, likelihood consensus, target tracking, wireless agent network

Electronic version of the publication:

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