The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Sequential Monte Carlo methods for multiple target tracking and data fusion
Abstract
The classical particle filter deals with the estimation of one state process conditioned on a realization of one observation process. We extend it here to the estimation of multiple state processes given realizations of several kinds of observation processes. The new algorithm is used to track with success multiple targets in a bearings-only context, whereas a JPDAF diverges. Making use of the ability of the particle filter to mix different types of observations, we then investigate how to join passive and active measurements for improved tracking.
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Cites
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
Cited by
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
Metadata
- Authors
- Hue, C., Le Cadre, J.-P., & Perez, P.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2002
- Volume
- 50
- Issue
- 2
- Pages
- 309-325
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2145555609
- Cited by
- 373
- References
- 3
- FWCI
- 18.40
- Type
- journal article
- Language
- en
- Open access
- closed
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