Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models

The probability hypothesis density (PHD) filter is a multiple- target filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementat...

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Bibliographic Details
Published in:	IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications pp. 19 - 28
Main Authors:	Clark, D, Ba-Tuong Vo, Ba-Ngu Vo, Godsill, S
Format:	Conference Proceeding
Language:	English
Published:	Stevenage IET 2008
Subjects:	Filtering methods in signal processing Interpolation and function approximation (numerical analysis) Other topics in statistics Signal processing theory clustering techniques probability Gaussian mixture implementations Kalman filter particle filtering (numerical methods) nonlinear filters recursively estimation target state estimates nonlinear dynamical systems state vectors Gaussian filtering techniques Gaussian mixture PHD filter multiple-target filter linear-Gaussian target dynamics Gaussian processes target tracking recursive estimation state estimation nonlinear filtering Kalman filters nonlinear dynamical models probability hypothesis density filters particle PHD filter
ISBN:	0863419100, 9780863419102
Online Access:	Get full text
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