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References (15)
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The Cardinalized Probability Hypothesis Density Filter for Linear Gaussian Multi-Target Models2006 40th Annual Conference on Information Sciences and Systems
- Publisher
- Springer Journals
- Copyright
- Copyright © 2013 by Institute of Applied Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg
- Subject
- Mathematics; Applications of Mathematics; Math Applications in Computer Science; Theoretical, Mathematical and Computational Physics
- ISSN
- 0168-9673
- eISSN
- 1618-3932
- DOI
- 10.1007/s10255-013-0252-0
- Publisher site
- See Article on Publisher Site
Abstract
The probability hypothesis density (PHD) propagates the posterior intensity in place of the posterior probability density of the multi-target state. The cardinalized PHD (CPHD) recursion is a generalization of PHD recursion, which jointly propagates the posterior intensity function and posterior cardinality distribution. A number of sequential Monte Carlo (SMC) implementations of PHD and CPHD filters (also known as SMCPHD and SMC-CPHD filters, respectively) for general non-linear non-Gaussian models have been proposed. However, these approaches encounter the limitations when the observation variable is analytically unknown or the observation noise is null or too small. In this paper, we propose a convolution kernel approach in the SMC-CPHD filter. The simulation results show the performance of the proposed filter on several simulated case studies when compared to the SMC-CPHD filter.
Journal
Acta Mathematicae Applicatae Sinica – Springer Journals
Published: Nov 29, 2013