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

Daniel Clark, Ba Tuong Vo, Ba N. Vo, Simon Godsill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

The Probability Hypothesis Density (PHD) filter is a multipletarget 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 implementations of this technique have been developed. The first of which, called the Particle PHD filter, requires clustering techniques to provide target state estimates which can lead to inaccurate estimates and is computationally expensive. The second algorithm, called the Gaussian Mixture PHD (GM-PHD) filter does not require clustering algorithms but is restricted to linear-Gaussian target dynamics, since it uses the Kalman filter to estimate the means and covariances of the Gaussians. This article provides a review of Gaussian filtering techniques for non-linear filtering and shows how these can be incorporated within the Gaussian mixture PHD filters. Finally, we show some simulated results of the different variants. © The Institution of Engineering and Technology.

Original languageEnglish
Title of host publicationIET Seminar on Target Tracking and Data Fusion: Algorithms and Applications
Pages19-28
Number of pages10
Volume2008
Edition12273
DOIs
Publication statusPublished - 2008
EventIET Seminar on Target Tracking and Data Fusion: Algorithms and Applications - Birmingham, United Kingdom
Duration: 15 Apr 200816 Apr 2008

Conference

ConferenceIET Seminar on Target Tracking and Data Fusion: Algorithms and Applications
CountryUnited Kingdom
CityBirmingham
Period15/04/0816/04/08

Fingerprint Dive into the research topics of 'Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models'. Together they form a unique fingerprint.

  • Cite this

    Clark, D., Vo, B. T., Vo, B. N., & Godsill, S. (2008). Gaussian mixture implementations of probability hypothesis density filters for non-linear dynamical models. In IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications (12273 ed., Vol. 2008, pp. 19-28) https://doi.org/10.1049/ic:20080053