BOOK-CHAPTER

Unscented Particle Implementation of Probability Hypothesis Density Filter for Multisensor Multitarget Tracking

Tianjun WuJianghong Ma

Year: 2012 Lecture notes in electrical engineering Pages: 321-326   Publisher: Springer Science+Business Media
Keywords:
Particle filter Tracking (education) Computer science Kalman filter Artificial intelligence Computer vision Psychology

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
7
Refs
0.06
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Target Tracking and Data Fusion in Sensor Networks
Physical Sciences →  Computer Science →  Artificial Intelligence
Inertial Sensor and Navigation
Physical Sciences →  Engineering →  Aerospace Engineering
Maritime Navigation and Safety
Physical Sciences →  Engineering →  Ocean Engineering

Related Documents

JOURNAL ARTICLE

Improved probability hypothesis density filter for multitarget tracking

Bo LiFuwen Pang

Journal:   Nonlinear Dynamics Year: 2013 Vol: 76 (1)Pages: 367-376
JOURNAL ARTICLE

Multiple-model Rao-Blackwellized particle probability hypothesis density filter for multitarget tracking

Bo Li

Journal:   International Journal of Control Automation and Systems Year: 2015 Vol: 13 (2)Pages: 426-433
JOURNAL ARTICLE

Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters

M. R. DanaeeFereidoon Behnia

Journal:   DOAJ (DOAJ: Directory of Open Access Journals) Year: 2017 Vol: 49 (1)Pages: 95-106
JOURNAL ARTICLE

A shrinkage probability hypothesis density filter for multitarget tracking

Huisi TongHao ZhangHuadong MengXiqin Wang

Journal:   EURASIP Journal on Advances in Signal Processing Year: 2011 Vol: 2011 (1)
© 2026 ScienceGate Book Chapters — All rights reserved.