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Proceedings Paper

2M-PDAF: an integrated two-model probabilistic data association filter
Author(s): X. Rong Li; Chen He
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Paper Abstract

The well-known probabilistic data association (PDA) filter handles the uncertainty in measurement origins inherent in tracking-in-clutter problems by using a probabilistically weighted sum of all measurements in the gate. In fact, the measurements in the gate may or may not include the one originated from the target. As such, two hypothetical models can be set up, corresponding to the events that the target measurements is and is not in the gate, respectively. This paper present an approach that integrates the PDA filter with the multiple-order method in a coherent manner based on the use of the above two hypothetical models. It is shown theoretically that the standard PDA filter is a special case of the first-order Generalized Pseudo Bayesian algorithm in the proposed formulation using a particular set of model transition probabilities. It is then proposed to adopt the superior interacting multiple-model architecture in this new formulation to improve the performance. The new algorithm is capable of achieving better performance by tuning the transition probabilities at a computational complexity comparable to that of the PDA filter. Simulation results are provided.

Paper Details

Date Published: 4 October 1999
PDF: 12 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364036
Show Author Affiliations
X. Rong Li, Univ. of New Orleans (United States)
Chen He, Univ. of New Orleans (United States)

Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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