JOURNAL ARTICLE

An unscented particle filter for ground maneuvering target tracking

Ronghua GuoZheng Qin

Year: 2007 Journal:   Journal of Zhejiang University. Science A Vol: 8 (10)Pages: 1588-1595   Publisher: Springer Science+Business Media

Abstract

In this study, an unscented particle filtering method based on an interacting multiple model (IMM) frame for a Markovian switching system is presented. The method integrates the multiple model (MM) filter with an unscented particle filter (UPF) by an interaction step at the beginning. The framework (interaction/mixing, filtering, and combination) is similar to that in a standard IMM filter, but an UPF is adopted in each model. Therefore, the filtering performance and degeneracy phenomenon of particles are improved. The filtering method addresses nonlinear and/or non-Gaussian tracking problems. Simulation results show that the method has better tracking performance compared with the standard IMM-type filter and IMM particle filter.

Keywords:
Tracking (education) Particle filter Particle (ecology) Kalman filter Control theory (sociology) Computer science Environmental science Physics Artificial intelligence Geology Psychology

Metrics

10
Cited By
1.16
FWCI (Field Weighted Citation Impact)
24
Refs
0.82
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Inertial Sensor and Navigation
Physical Sciences →  Engineering →  Aerospace Engineering
Underwater Acoustics Research
Physical Sciences →  Earth and Planetary Sciences →  Oceanography

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