Please use this identifier to cite or link to this item: https://doi.org/10.1109/IMTC.2004.1351092
Title: Improved particle filter in sensor fusion for tracking random moving object
Authors: Jing, L.
Vadakkepat, P. 
Keywords: Particle Filter
Random Moving Object Tracking
Sensor Fusion
Issue Date: 2004
Source: Jing, L.,Vadakkepat, P. (2004). Improved particle filter in sensor fusion for tracking random moving object. Conference Record - IEEE Instrumentation and Measurement Technology Conference 1 : 476-481. ScholarBank@NUS Repository. https://doi.org/10.1109/IMTC.2004.1351092
Abstract: Non-linear and non-Gaussian estimation is a challenging problem in multi-sensor fusion. To handle this, particle filter is used to estimate the system state based on information from camera and sonar sensor. The state variables such as position, velocity and acceleration of a random moving object change very quickly and are hard to track. This leads to serious sample impoverishment in particle tracking algorithm. In this paper, a new resampling algorithm is presented. Random samples are drawn from the neighborhoods of previous samples with high weights and the effect of sample impoverishment is reduced. The state space model is augmented with acceleration variables to describe the random movement more accurately.
Source Title: Conference Record - IEEE Instrumentation and Measurement Technology Conference
URI: http://scholarbank.nus.edu.sg/handle/10635/70556
ISBN: 078038248X
ISSN: 10915281
DOI: 10.1109/IMTC.2004.1351092
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