Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression
For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gau...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2018-01-01
|
| Series: | MATEC Web of Conferences |
| Online Access: | https://doi.org/10.1051/matecconf/201817601017 |
| _version_ | 1818413682709757952 |
|---|---|
| author | Chi Luo-jia Feng Xin-xi Miao Lu |
| author_facet | Chi Luo-jia Feng Xin-xi Miao Lu |
| author_sort | Chi Luo-jia |
| collection | DOAJ |
| description | For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking. |
| first_indexed | 2024-12-14T11:07:05Z |
| format | Article |
| id | doaj.art-ee44ae801ceb4c96aad00c510fe977ab |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-14T11:07:05Z |
| publishDate | 2018-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-ee44ae801ceb4c96aad00c510fe977ab2022-12-21T23:04:28ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-011760101710.1051/matecconf/201817601017matecconf_ifid2018_01017Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process RegressionChi Luo-jiaFeng Xin-xiMiao LuFor the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking.https://doi.org/10.1051/matecconf/201817601017 |
| spellingShingle | Chi Luo-jia Feng Xin-xi Miao Lu Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression MATEC Web of Conferences |
| title | Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression |
| title_full | Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression |
| title_fullStr | Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression |
| title_full_unstemmed | Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression |
| title_short | Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression |
| title_sort | generalized labeled multi bernoulli extended target tracking based on gaussian process regression |
| url | https://doi.org/10.1051/matecconf/201817601017 |
| work_keys_str_mv | AT chiluojia generalizedlabeledmultibernoulliextendedtargettrackingbasedongaussianprocessregression AT fengxinxi generalizedlabeledmultibernoulliextendedtargettrackingbasedongaussianprocessregression AT miaolu generalizedlabeledmultibernoulliextendedtargettrackingbasedongaussianprocessregression |