Name
Abstract | ||
|---|---|---|
This paper presents a Bayesian algorithm for single target tracking using state mixture model theory. Compared with the existing approaches, the proposed algorithm aims at deriving the likelihood function of all measurements. Given this, an analytic Bayesian algorithm is further proposed. Moreover, under linear Gaussian assumptions on the dynamics and measurement model, a closed-form solution is proposed. Our study demonstrates the effectiveness of the proposed method in single target detection and tracking. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.sigpro.2012.01.006 | Signal Processing |
Keywords | Field | DocType |
closed-form solution,state mixture model theory,bayesian algorithm,existing approach,single target tracking,measurement model,bayesian estimation,single target detection,proposed algorithm,analytic bayesian algorithm,mixture model,likelihood function | Bayesian algorithm,Likelihood function,Pattern recognition,Marginal likelihood,Gaussian,Artificial intelligence,Bayes estimator,Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
92 | 7 | 0165-1684 |
Citations | PageRank | References |
3 | 0.41 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weifeng Liu | 1 | 16 | 1.92 |
| Chenglin Wen | 2 | 179 | 42.72 |
| Chongzhao Han | 3 | 446 | 71.68 |
| Feng Lian | 4 | 14 | 3.54 |