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Michael Beard
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Journal Articles
- 2020
- [j9]Michael Beard
, Ba-Tuong Vo
, Ba-Ngu Vo
:
A Solution for Large-Scale Multi-Object Tracking. IEEE Trans. Signal Process. 68: 2754-2769 (2020) - 2019
- [j8]Ba-Ngu Vo
, Ba-Tuong Vo
, Michael Beard
:
Multi-Sensor Multi-Object Tracking With the Generalized Labeled Multi-Bernoulli Filter. IEEE Trans. Signal Process. 67(23): 5952-5967 (2019) - 2017
- [j7]Michael Beard, Ba-Tuong Vo
, Ba-Ngu Vo
, Sanjeev Arulampalam:
Void Probabilities and Cauchy-Schwarz Divergence for Generalized Labeled Multi-Bernoulli Models. IEEE Trans. Signal Process. 65(19): 5047-5061 (2017) - 2016
- [j6]Branko Ristic
, Michael Beard, Claudio Fantacci
:
An overview of particle methods for random finite set models. Inf. Fusion 31: 110-126 (2016) - [j5]Michael Beard, Stephan Reuter, Karl Granström, Ba-Tuong Vo
, Ba-Ngu Vo
, Alexander Scheel:
Multiple Extended Target Tracking With Labeled Random Finite Sets. IEEE Trans. Signal Process. 64(7): 1638-1653 (2016) - 2015
- [j4]Michael Beard, Ba-Tuong Vo
, Ba-Ngu Vo
:
Bayesian Multi-Target Tracking With Merged Measurements Using Labelled Random Finite Sets. IEEE Trans. Signal Process. 63(6): 1433-1447 (2015) - [j3]Francesco Papi, Ba-Ngu Vo
, Ba-Tuong Vo
, Claudio Fantacci
, Michael Beard:
Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities. IEEE Trans. Signal Process. 63(20): 5487-5497 (2015) - 2013
- [j2]Michael Beard, Ba-Tuong Vo
, Ba-Ngu Vo
:
Multitarget Filtering With Unknown Clutter Density Using a Bootstrap GMCPHD Filter. IEEE Signal Process. Lett. 20(4): 323-326 (2013) - [j1]Michael Beard, Ba-Tuong Vo
, Ba-Ngu Vo
, Sanjeev Arulampalam:
A Partially Uniform Target Birth Model for Gaussian Mixture PHD/CPHD Filtering. IEEE Trans. Aerosp. Electron. Syst. 49(4): 2835-2844 (2013)
Conference and Workshop Papers
- 2018
- [c11]Michael Beard, Ba-Tuong Vo
, Ba-Ngu Vo
:
Performance Evaluation for Large-Scale Multi-Target Tracking Algorithms. FUSION 2018: 1-5 - 2017
- [c10]Michael Beard, Ba-Tuong Vo
, Ba-Ngu Vo
:
OSPA(2): Using the OSPA metric to evaluate multi-target tracking performance. ICCAIS 2017: 86-91 - 2016
- [c9]Michael Beard, Ba-Tuong Vo, Ba-Ngu Vo:
Generalised labelled multi-Bernoulli forward-backward smoothing. FUSION 2016: 688-694 - 2015
- [c8]Stephan Reuter, Alexander Scheel, Michael Beard, Karl Granström, Ba-Tuong Vo, Ba-Ngu Vo, Klaus Dietmayer:
Environment Perception for Companion Systems. ISCT 2015: 68-72 - [c7]Michael Beard, Ba-Tuong Vo, Ba-Ngu Vo, Sanjeev Arulampalam:
Sensor control for multi-target tracking using Cauchy-Schwarz divergence. FUSION 2015: 937-944 - [c6]Michael Beard, Stephan Reuter, Karl Granström, Ba-Tuong Vo, Ba-Ngu Vo, Alexander Scheel:
A generalised labelled multi-Bernoulli filter for extended multi-target tracking. FUSION 2015: 991-998 - [c5]Stephan Reuter, Michael Beard, Karl Granström, Klaus Dietmayer:
Tracking extended targets in high clutter using a GGIW-LMB filter. SDF 2015: 1-6 - 2014
- [c4]Michael Beard, Ba-Tuong Vo, Ba-Ngu Vo:
Multi-target tracking with merged measurements using labelled random finite sets. FUSION 2014: 1-8 - 2012
- [c3]Michael Beard, Ba-Tuong Vo, Ba-Ngu Vo, Sanjeev Arulampalam:
Gaussian mixture PHD and CPHD filtering with partially uniform target birth. FUSION 2012: 535-541 - [c2]Michael Beard, Sanjeev Arulampalam:
Performance of PHD and CPHD filtering versus JIPDA for bearings-only multi-target tracking. FUSION 2012: 542-549 - 2007
- [c1]Michael Beard, Sanjeev Arulampalam:
Comparison of data association algorithms for bearings-only multi-sensor multi-target tracking. FUSION 2007: 1-7
Informal and Other Publications
- 2016
- [i1]Branko Ristic, Michael Beard, Claudio Fantacci:
An Overview of Particle Methods for Random Finite Set Models. CoRR abs/1602.03945 (2016)
Coauthor Index
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