Mahler’s Probability Hypothesis Density (PHD filter), proposed in 2000, addresses the challenges of the multipletarget
detection and tracking problem by propagating a mean density of the targets in any region of the state
space. However, when retrieving some local evidence on the target presence becomes a critical component of
a larger process - e.g. for sensor management purposes - the local target number is insufficient unless some
confidence on the estimation of the number of targets can be provided as well. In this paper, we propose a
first implementation of a PHD filter that also includes an estimation of localised variance in the target number
following each update step; we then illustrate the advantage of the PHD filter + variance on simulated data from
a multiple-target scenario.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.