Name
Abstract | ||
|---|---|---|
Data association, which involves the assignment of one collection of objects to another, is an important problem in multiple target tracking. Exact computation of data association probabilities is not always computationally feasible, in particular when many targets are in close proximity and share many measurements. In, this paper a Monte Carlo method for approximation of data association probabilities in such situations is proposed. The proposed method is a refinement of an existing importance sampling method for matrix permanent approximation. It is shown via numerical simulations that the proposed method can accurately approximate data association probabilities in dense multiple target scenarios with reasonable computational expense. |
Year | Venue | Keywords |
|---|---|---|
2009 | Fusion | Monte Carlo methods,matrix algebra,sensor fusion,target tracking,Monte Carlo method,dense multiple target scenarios,importance sampling,joint data association,matrix permanent approximation,multiple target tracking,Data association,Monte Carlo methods |
Field | DocType | Citations |
Importance sampling,Joint Probabilistic Data Association Filter,Computer simulation,Computer science,Artificial intelligence,Computation,Approximation algorithm,Mathematical optimization,Monte Carlo method,Algorithm,Sensor fusion,Machine learning,Computational complexity theory | Conference | 1 |
PageRank | References | Authors |
0.35 | 2 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark R. Morelande | 1 | 195 | 24.96 |