Data association combined with the probability hypothesis density filter for multitarget tracking

Lin Lin; Yaakov Bar-Shalom; Thiagalingam Kirubarajan

doi:10.1117/12.542218

25 August 2004 Data association combined with the probability hypothesis density filter for multitarget tracking

Lin Lin, Yaakov Bar-Shalom, Thiagalingam Kirubarajan

Proceedings Volume 5428, Signal and Data Processing of Small Targets 2004; (2004) https://doi.org/10.1117/12.542218
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Multiple target tracking requires data association that operates in conjunction with filtering. When multiple targets are closely spaced, the conventional approach (MHT/assignment) may not give satisfactory results. This is mainly because of the difficulty in deciding what the number of targets is. Recently, the probability hypothesis density (PHD) filter, which propagates the PHD or the first moment instead of the full multitarget posterior density, was proposed. In this approach, the integral of the PHD over a region in the state space is the expected number of targets within this region and the peaks in the PHD can be regarded as the estimated locations of the targets at a given time step. In this approach the data association problem is not considered, i.e., the PHD is obtained only for a frame at a time. In our paper, a data association method combined with the PHD approach is proposed for multitarget tracking, i.e., we keep a separate tracker for each target, use the PHD filter to get the estimated number and locations of the targets at each time step, and then perform the "peak-to-track" association, whose results can provide information for PHD peak extraction at the next time step. Besides, by keeping a separate tracker for each target, our approach provides more information than the standard PHD filter. Using our approach, the multitarget tracking can be performed with automatic track initiation, maintenance, spawning, merging and termination. Simulation results demonstrate that our approach is computationally feasible and effective.

Citation Download Citation

Lin Lin, Yaakov Bar-Shalom, and Thiagalingam Kirubarajan "Data association combined with the probability hypothesis density filter for multitarget tracking", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); https://doi.org/10.1117/12.542218

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