CPHD and PHD filters for unknown backgrounds I: dynamic data clustering

Ronald Mahler

doi:10.1117/12.818022

6 May 2009 CPHD and PHD filters for unknown backgrounds I: dynamic data clustering

Ronald Mahler

Proceedings Volume 7330, Sensors and Systems for Space Applications III; 73300K (2009) https://doi.org/10.1117/12.818022
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

The probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters were introduced in 2000 and 2006, respectively, as approximations of the full multitarget Bayes detection and tracking filter. Both filters are based on the "standard" multitarget measurement model that underlies most multitarget tracking theory. This paper is part of a series of theoretical studies that addresses PHD and CPHD filters for nonstandard multitarget measurement models. In this paper I derive the measurement-update equations for CPHD and PHD filters that estimate models of unknown, dynamically changing data, such as background clutter. A companion paper generalizes these results to multitarget detection and tracking in unknown, dynamic clutter.

Citation Download Citation

Ronald Mahler "CPHD and PHD filters for unknown backgrounds I: dynamic data clustering", Proc. SPIE 7330, Sensors and Systems for Space Applications III, 73300K (6 May 2009); https://doi.org/10.1117/12.818022

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available