Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Karl Granstrom"'
Publikováno v:
IEEE Access, Vol 8, Pp 126414-126427 (2020)
A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized
Externí odkaz:
https://doaj.org/article/993b882b13fc4db2903ebfe93b32a7ab
Autor:
Yuxuan Xia, Lennart Svensson, Angel F. Garcia-Fernandez, Jason L. Williams, Daniel Svensson, Karl Granstrom
Publikováno v:
IEEE Transactions on Signal Processing
This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an av
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81bd525086ee2f9b5e948b756a7f7648
https://doi.org/10.1109/tsp.2022.3184794
https://doi.org/10.1109/tsp.2022.3184794
Autor:
Yuxuan Xia, Pu Wang, Karl Granstrom, Karl Berntorp, Hassan Mansour, Petros T. Boufounos, Philip Orlik, Lennart Svensson
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 15:1013-1029
This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian (HTG) with structura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fefce8ac637c6f3818befbcb385548da
https://doi.org/10.1109/jstsp.2021.3058062
https://doi.org/10.1109/jstsp.2021.3058062
Publikováno v:
IEEE Transactions on Intelligent Vehicles. 4:609-621
In a typical multitarget tracking (MTT) scenario, the sensor state is either assumed known, or tracking is performed in the sensor's (relative) coordinate frame. This assumption does not hold when the sensor, e.g., an automotive radar, is mounted on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e803cafe8a321751bff58738b233d079
https://doi.org/10.1109/tiv.2019.2938093
https://doi.org/10.1109/tiv.2019.2938093
Autor:
Petros T. Boufounos, Karl Granstrom, Pu Wang, Lennart Svensson, Karl Berntorp, Yuxuan Xia, Philip Orlik
Publikováno v:
2020 IEEE Radar Conference (RadarConf20).
This paper presents a data-driven measurement model for extended object tracking (EOT) with automotive radar. Specifically, the spatial distribution of automotive radar measurements is modeled as a hierarchical truncated Gaussian with structural geom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02a937b6f35372284c8e522b494e28f3
https://doi.org/10.1109/radarconf2043947.2020.9266598
https://doi.org/10.1109/radarconf2043947.2020.9266598
Publikováno v:
FUSION
This paper presents two multi-Bernoulli filters on sets of trajectories for multiple target tracking. The first filter provides a multi-Bernoulli approximation of the posterior density over the set of alive trajectories at the current time step. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::466e27eeb926fd0b541bfeeb993c41e7
https://doi.org/10.23919/fusion45008.2020.9190554
https://doi.org/10.23919/fusion45008.2020.9190554
Publikováno v:
ICC
5G is expected to enable simultaneous vehicle localization and environment mapping (SLAM). Furthermore, vehicular networks will be covered with 5G small cells, wherein the map information is collected at each base station (BS) and then fused so as to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f2dd2d495f87ad7112a0da1c94a5ab2
https://doi.org/10.1109/icc40277.2020.9149211
https://doi.org/10.1109/icc40277.2020.9149211
Publikováno v:
ICASSP
In 5G mmWave, simultaneous localization and mapping (SLAM) allows devices to exploit map information to improve their position estimate. Even the most basic SLAM filter based on a Rao-Blackwellized particle filter (RBPF) combined with a probability h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::209a38afd3e7bc82cc1c0964d732f5a0
https://doi.org/10.1109/icassp40776.2020.9053132
https://doi.org/10.1109/icassp40776.2020.9053132
Publikováno v:
FUSION
This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebcd9fb9bf89b56c05bf53b8681b5829
Publikováno v:
FUSION
In this paper we introduce spatiotemporal constraints for trajectories, i.e., restrictions that the trajectory must be in some part of the state space (spatial constraint) at some point in time (temporal constraint). Spatiotemporal contraints on traj
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::62dd7bf66f0fd012e6948759313ab2ed