Results 181 to 190 of about 513,541 (222)
S-shaped Utility Maximization with VaR Constraint and Partial Information. [PDF]
J Optim Theory ApplZhu D, Davey A, Zheng H.
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Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach. [PDF]
Biom JCastro-Prado F +4 more
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The Labeled Multi-Bernoulli Filter
IEEE Transactions on Signal Processing, 2014 This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled ...
Klaus Dietmayer +2 more
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Multi-Scan Generalized Labeled Multi-Bernoulli Filter
2018 21st International Conference on Information Fusion (FUSION), 2018 This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagates the labeled multi-target posterior density.
Ba-Tuong Vo, Ba-Ngu Vo
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A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation
IEEE Transactions on Aerospace and Electronic Systems, 2020 We propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association. The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the Performance is ...
Florian Meyer +2 more
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Robust Multi-Bernoulli Filtering
IEEE Journal of Selected Topics in Signal Processing, 2013In Bayesian multi-target filtering knowledge of parameters such as clutter intensity and detection probability profile are of critical importance. Significant mismatches in clutter and detection model parameters results in biased estimates. In this paper we propose a multi-target filtering solution that can accommodate non-linear target models and an ...
Ba-Tuong Vo +3 more
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Poisson Multi-Bernoulli Mixture Filter: Direct Derivation and Implementation [PDF]
IEEE Transactions on Aerospace and Electronic Systems, 2018 We provide a derivation of the Poisson multi-Bernoulli mixture (PMBM) filter for multitarget tracking with the standard point target measurements without using probability generating functionals or functional derivatives. We also establish the connection
Jason Williams +2 more
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IEEE Transactions on Circuits and Systems II: Express Briefs, 2014
Selective filters are obtained by the approximation of the rectangular magnitude. Classic approximation methods employ polynomials or rational functions. Modern methods are based on numerical optimization. The optimization-based approach is effective and gives the designer much freedom.
Goran Molnar, Mladen Vucic
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Selective filters are obtained by the approximation of the rectangular magnitude. Classic approximation methods employ polynomials or rational functions. Modern methods are based on numerical optimization. The optimization-based approach is effective and gives the designer much freedom.
Goran Molnar, Mladen Vucic
openaire +3 more sources
Event-Triggered Consensus Bernoulli Filtering
2018 21st International Conference on Information Fusion (FUSION), 2018This paper focuses on reducing communication bandwidth and, consequently, energy consumption in the context of distributed target detection and tracking over a peer-to-peer sensor network. A consensus Bernoulli filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when a ...
Lin Gao +3 more
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The Labeled Multi-Bernoulli SLAM Filter
IEEE Signal Processing Letters, 2015In this contribution, a new algorithm addressing the simultaneous localization and mapping (SLAM) problem is proposed: a Rao-Blackwellized implementation of the Labeled Multi-Bernoulli SLAM (LMB-SLAM) filter. Further, we establish that the LMB-SLAM does not require the approximations used in Probability Hypothesis Density SLAM (PHD-SLAM).
Hendrik Deusch +2 more
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