Application of an improved particle filter for state estimation

A novel Gaussian mixture sigma-point particle filter algorithm is proposed to mitigate the sample depletion problem. The posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectati...

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Veröffentlicht in:Chinese Control Conference S. 489 - 493
Hauptverfasser: Xiang Li, Liu Yu, Su Baoku
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2008
Schlagworte:
Atmospheric measurements
Bearing only tracking
Equations
Estimation algorithm
Filtering
Mathematical model
Monte Carlo methods
Monte carlo simulation
Noise
Particle filter
Particle measurements
ISSN:1934-1768
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Zusammenfassung:A novel Gaussian mixture sigma-point particle filter algorithm is proposed to mitigate the sample depletion problem. The posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. The simulation results demonstrate the validity of the proposed algorithm.
ISSN:1934-1768
DOI:10.1109/CHICC.2008.4604962