Maneuvering target tracking using probability hypothesis density smoothing

N. Nadarajah; T. Kirubarajan

doi:10.1117/12.817630

11 May 2009 Maneuvering target tracking using probability hypothesis density smoothing

N. Nadarajah, T. Kirubarajan

Proceedings Volume 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII; 73360F (2009) https://doi.org/10.1117/12.817630
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

The Probability Hypothesis Density (PHD) filter is a computationally tractable alternative to the optimal nonlinear filter. The PHD filter propagates the first moment instead of the full posterior density. Evaluation of the PHD enables one to extract the number of targets as well as their individual states from noisy data with data association uncertainties. Recently, a smoothing algorithm was proposed by the authors to improve the capability of PHD based tracking. Smoothing produces delayed estimates, which yield better estimates not only for the target states but also for the unknown number of targets. However, in the case of the maneuvering target tracking problem, this single model method may not provide accurate estimates. In this paper, a multiple model PHD smoothing method is proposed to improve the tracking of multiple maneuvering targets. A fast sequential Monte Carlo implementation for a special case is also provided. Simulations are performed with the proposed method consisting of multiple maneuvering targets. Simulation results confirm the improved performance of the proposed algorithm.

Citation Download Citation

N. Nadarajah and T. Kirubarajan "Maneuvering target tracking using probability hypothesis density smoothing", Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 73360F (11 May 2009); https://doi.org/10.1117/12.817630

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