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The Poisson Multi-Bernoulli Mixture (PMBM) density is a conjugate multi-target density for the standard point target model with Poisson point process birth.
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B. N. Vo and B. T. Vo, “A multi-scan labeled random finite set model for multi-object state estimation,” IEEE Trans. Signal Process. , vol. 67, no. 19, pp. 4948–4963, Oct. 2019
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Y. Xia, K. Granström, L. Svensson, A. F. García-Fernández, and J. L. Williams, “Multi-scan implementation of the trajectory Poisson multi-Bernoulli mixture filter,” Jour. Adv. Inform. Fusion , vol. 14, no. 2, pp. 213–235, Dec. 2019
2019
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2014
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2014
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2015
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2015
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A. Milan, K. Schindler, and S. Roth, “Multi-target tracking by discrete-continuous energy minimization,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 38, no. 10, pp. 2054–2068, 2016
2016
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S. Mori, C.-Y. Chong, and K. C. Chang, “Three formalisms of multiple hypothesis tracking,” in Proc. 19th International Conference on Information Fusion , July 2016
2016
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2016
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D. F. Crouse, “On implementing 2D rectangular assignment algorithms,” IEEE Transactions on Aerospace and Electronic Systems , vol. 52, no. 4, pp. 1679–1696, August 2016
2016
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A. F. García-Fernández and L. Svensson, “Trajectory PHD and CPHD filters,” IEEE Trans. Signal Process. , vol. 67, no. 22, pp. 5702–5714, Nov. 2019
2019
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2020
Closest in time.
A. F. García-Fernández, L. Svensson, J. L. Williams, Y. Xia, and K. Granström, “Trajectory Poisson multi-Bernoulli filters,” IEEE Transactions on Signal Processing , vol. 68, pp. 4933–4945, 2020
2020
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M. Beard, B. T. Vo, and B. Vo, “A solution for large-scale multi-object tracking,” IEEE Transactions on Signal Processing , vol. 68, pp. 2754–2769, 2020
2020
Closest in time.
K. Granström, L. Svensson, Y. Xia, A. F. García-Fernández, and J. L. Williams, “Spatiotemporal constraints for sets of trajectories with applications to PMBM densities,” in 23rd International Conference on Information Fusion , 2020, pp. 1–8
2020
Closest in time.
K. Granström, M. Fatemi, and L. Svensson, “Poisson multi-Bernoulli conjugate prior for multiple extended object filtering,” IEEE Trans. Aerosp. Electron. Syst. , vol. 56, no. 1, pp. 208–225, 2020
2020
Closest in time.
A. F. García-Fernández and S. Maskell, “Continuous-discrete multiple target filtering: PMBM, PHD and CPHD filter implementations,” IEEE Transactions on Signal Processing , vol. 68, pp. 1300–1314, 2020
2020
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Á. F. García-Fernández, A. S. Rahmathullah, and L. Svensson, “A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms,” IEEE Transactions on Signal Processing , vol. 68, pp. 3917–3928, 2020
2020
Closest in time.
J. Zhou, T. Li, X. Wang, and L. Zheng, “Target tracking with equality/inequality constraints based on trajectory function of time,” IEEE Signal Processing Letters , vol. 28, pp. 1330–1334, 2021
2021
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E. F. Brekke, A. G. Hem, and L.-C. N. Tokle, “Multitarget tracking with multiple models and visibility: Derivation and verification on maritime radar data,” IEEE Journal of Oceanic Engineering , vol. 46, no. 4, pp. 1272–1287, 2021
2021
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P. Boström-Rost, D. Axehill, and G. Hendeby, “Sensor management for search and track using the Poisson multi-Bernoulli mixture filter,” IEEE Transactions on Aerospace and Electronic Systems , vol. 57, no. 5, pp. 2771–2783, 2021
2021
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S. Pang and H. Radha, “Multi-object tracking using Poisson multi-Bernoulli mixture filtering for autonomous vehicles,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 7963–7967
2021
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A. F. García-Fernández and W. Yi, “Continuous-discrete multiple target tracking with out-of-sequence measurements,” IEEE Transactions on Signal Processing , vol. 69, pp. 4699–4709, 2021
2021
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P. Boström-Rost, D. Axehill, and G. Hendeby, “PMBM filter with partially grid-based birth model with applications in sensor management,” IEEE Transactions on Aerospace and Electronic Systems , vol. 58, no. 1, pp. 530–540, 2022
2022
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J. Zhao, P. Wu, X. Liu, Y. Xu, L. Mihaylova, S. Godsill, and W. Wang, “Audio-visual tracking of multiple speakers via a PMBM filter,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2022, pp. 5068–5072
2022
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Y. Ge, O. Kaltiokallio, H. Kim, F. Jiang, J. Talvitie, M. Valkama, L. Svensson, S. Kim, and H. Wymeersch, “A computationally efficient EK-PMBM filter for bistatic mmWave radio SLAM,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 7, pp. 2179–2192, 2022
2022
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H. Kim, K. Granstrom, L. Svensson, S. Kim, and H. Wymeersch, “PMBM-based SLAM filters in 5G mmwave vehicular networks,” IEEE Transactions on Vehicular Technology , vol. 71, no. 8, pp. 8646–8661, 2022
2022
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A. F. García-Fernández, Y. Xia, and L. Svensson, “A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth,” in 25th International Conference on Information Fusion , 2022
2022
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A. F. García-Fernández and L. Svensson, “Tracking multiple spawning targets using Poisson multi-Bernoulli mixtures on sets of tree trajectories,” IEEE Transactions on Signal Processing , vol. 70, pp. 1987–1999, 2022
2022
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Y. Xia, Á. F. García-Fernández, F. Meyer, J. L. Williams, K. Granström, and L. Svensson, “Trajectory PMB filters for extended object tracking using belief propagation,” IEEE Transactions on Aerospace and Electronic Systems , vol. 59, no. 6, pp. 9312–9331, 2023
2023
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