Rao-Blackwellised approximate conditional mean probability hypothesis density filtering

N. Nandakumaran; S. Sutharsan; R. Tharmarasa; T. Lang; T. Kirubarajan

doi:10.1117/12.826423

3 September 2009 Rao-Blackwellised approximate conditional mean probability hypothesis density filtering

N. Nandakumaran, S. Sutharsan, R. Tharmarasa, T. Lang, T. Kirubarajan

Proceedings Volume 7445, Signal and Data Processing of Small Targets 2009; 74450J (2009) https://doi.org/10.1117/12.826423
Event: SPIE Optical Engineering + Applications, 2009, San Diego, California, United States

Abstract

In this paper, a new state estimation algorithm for estimating the states of targets that are separable into linear and nonlinear subsets with non-Gaussian observation noise distributed according to a mixture of Gaussian functions is proposed. The approach involves modeling the collection of targets and measurements as random finite sets and applying a new Rao-Blackwellised Approximate Conditional Mean Probability Hypothesis Density (RB-ACM-PHD) recursion to propagate the posterior density. The RB-ACM-PHD filter jointly estimates the time-varying number of targets and the observation sets in the presence of data association uncertainty, detection uncertainty, noise and false alarms. The proposed algorithm approximates a mixture Gaussian distribution with a moment-matched Gaussian in the weight update phase of the filtering recursion. A two dimensional maneuvering target tracking example is used to evaluate the merits of the proposed algorithm. The RB-ACM-PHD filter results in a significant reduction in computation time while maintaining filter accuracies similar to the standard sequential Monte Carlo PHD implementation.

Citation Download Citation

N. Nandakumaran, S. Sutharsan, R. Tharmarasa, T. Lang, and T. Kirubarajan "Rao-Blackwellised approximate conditional mean probability hypothesis density filtering", Proc. SPIE 7445, Signal and Data Processing of Small Targets 2009, 74450J (3 September 2009); https://doi.org/10.1117/12.826423

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