An Efficient Implementation of the Generalized Labeled Multi-Bernoulli Filter

This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to an earlier implementation that involves separate truncations in the prediction and update steps, the proposed implementat...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on signal processing Ročník 65; číslo 8; s. 1975 - 1987
Hlavní autoři: Vo, Ba-Ngu, Vo, Ba-Tuong, Hoang, Hung Gia
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 15.04.2017
Témata:
Complexity theory
data association
generalized labeled multi-Bernoulli
Gibbs sampling
Indexes
multiobject tracking
optimal assignment
Prediction algorithms
Radar tracking
Radio frequency
Random finite sets
ranked assignment
Signal processing algorithms
Trajectory
ISSN:1053-587X, 1941-0476
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:This paper proposes an efficient implementation of the generalized labeled multi-Bernoulli (GLMB) filter by combining the prediction and update into a single step. In contrast to an earlier implementation that involves separate truncations in the prediction and update steps, the proposed implementation requires only one truncation procedure for each iteration. Furthermore, we propose an efficient algorithm for truncating the GLMB filtering density based on Gibbs sampling. The resulting implementation has a linear complexity in the number of measurements and quadratic in the number of hypothesized objects.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2016.2641392