The Cardinality Balanced Multi-Target Multi-Bernoulli Filter and Its Implementations

By B. Vo, Ba-Ngu Vo, Antonio Cantoni. IEEE Transactions on Signal Processing, 2009

878 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Oceanography

View Paper: https://www.semanticscholar.org/paper/800296522d60e8aec816162a8255836818b860d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. B. Vo
2. Hung Gia Hoang
3. Jason L. Williams
4. Karl Granström
5. Dinh Q. Phung
6. Marcus Baum
7. Ba-Ngu Vo
8. B. Ristic

In Computer Networks and Communications

1. B. Vo
2. Jason L. Williams
3. Hung Gia Hoang
4. Karl Granström
5. Marcus Baum
6. Dinh Q. Phung
7. Ba-Ngu Vo
8. B. Ristic

In Oceanography

1. Jason L. Williams
2. Dinh Q. Phung
3. Ba-Ngu Vo
4. B. Vo
5. Hung Gia Hoang
6. B. Ristic
7. Karl Granström
8. Marcus Baum

Citations in the corpus

• The Labeled Multi-Bernoulli Filter (2014, methodology): “PREMISE OF THE LABELED MULTI-BERNOULLI FILTER In [10], an approximation to the Bayes multi-target filter was proposed based on approximating the posterior as a multi-Bernoulli RFS.”
• A Random-Finite-Set Approach to Bayesian SLAM (2011, methodology): “For instance, the Poisson assumption of the PHD approach can be relaxed via the cardinalized PHD construct [37] and the multi-Bernoulli recursion [ 38 ].”
• Labeled Random Finite Sets and Multi-Object Conjugate Priors (2013, background): “Implementations of these filters are developed in [23]–[26], [22], with convergence analysis given in [23], [27]–[30].”
• A Tutorial on Bernoulli Filters: Theory, Implementation and Applications (2013, methodology): “In the case of the detector-output model and point targets, an approximate Multi-Bernoulli filter was proposed in [10], [25].”
• Joint Detection and Estimation of Multiple Objects From Image Observations (2010, methodology): “The Multi-Bernoulli approach avoids this problem altogether [ 25 ].”
• An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter (2016, methodology): “The RFS approach has attracted significant attention as a general systematic treatment of multi-object systems and provides the foundation for the development of novel filters such as the Probability Hypothesis Density (PHD) filter [5], Cardinalized PHD (CPHD) filter [6], and multi-Bernoulli filters”
• Extended Object Tracking: Introduction, Overview and Applications (2016, methodology): “2) CB-MeMBer filters: An extension of the CB-MeMBer filter [195] to extended objects, using the PPP mea-”
• Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter (2013, methodology): “Due to the numerical complexity of Bayes multi-target filter, the Probability Hypothesis Density (PHD) [8], Cardinalized PHD (CPHD) [29], and multi-Bernoulli filters [30], [9] have been developed as approximations.”
• Marginal multi-bernoulli filters: RFS derivation of MHT, JIPDA, and association-based member (2012, methodology): “The MeMBer makes approximations in its derivation that avoid association and are reasoned to be of little consequence when the false alarm rate is low; these approximations introduced a significant cardinality bias that was corrected in the cardinality balanced MeMBer (CB-MeMBer) [6].”