A Mixed Target Tracking Algorithm with Type Probability Using the GLMB filter
| Autor: | Wang Zhi, Liu Weifeng, Huang Zilong, Xinfeng Ru |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Radar tracker
Computer science Scattering 010401 analytical chemistry 02 engineering and technology Filter (signal processing) 021001 nanoscience & nanotechnology Tracking (particle physics) Ellipse 01 natural sciences 0104 chemical sciences symbols.namesake symbols 0210 nano-technology Focus (optics) Point target Algorithm Gibbs sampling |
| Zdroj: | 2020 Chinese Automation Congress (CAC). |
| DOI: | 10.1109/cac51589.2020.9326925 |
| Popis: | This paper focus on the state estimation, number of targets estimation and target type estimation of mixed targets and propose a mixed target tracking algorithm with type probability using the GLMB filter. The mixed targets include point target, extended target and group target (indistinguishable). The tracking algorithm mainly includes three aspects: mixed target dynamic modeling, type analysis and tracking estimation. Firstly, measurement model of mixed target is established with the generalized label multi-Bernoulli filter. Secondly, the probability of targets type are calculated. Finally, the parameters of finite mixture models (FMM) are derived by using Gibbs sampling and BIC criterion to track the mixed target. Then the equivalent measurement method is used to replace the measurement of extended target and group target. The shape of the mixed target is estimated by the ellipse approximation modeling method. Simulation experiments are provided to show the effectively of track mixed targets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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