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Target tracking with unknown noise statistics based on intelligent H∞ particle filter
Author(s) -
Havangi R.
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2872
Subject(s) - particle filter , particle swarm optimization , degeneracy (biology) , tracking (education) , noise (video) , monte carlo method , filter (signal processing) , kalman filter , monte carlo localization , computer science , particle (ecology) , feature (linguistics) , algorithm , control theory (sociology) , mathematical optimization , artificial intelligence , statistics , mathematics , computer vision , bioinformatics , psychology , pedagogy , oceanography , linguistics , philosophy , control (management) , image (mathematics) , biology , geology
Summary In this paper, the target tracking based on the H∞ unscented particle filter and the particle swarm optimization is proposed. The proposed algorithm combines unscented particle filter and H∞ filter to estimate the target state. Furthermore, to prevent the particle degeneracy and impoverishment, particle swarm optimization is adapted to optimize particles. The proposed method has the common advantageous feature that it does not need to know the noise statistics. The performance of the proposed algorithm is shown through Monte Carlo runs and its performance is compared with that of other methods.