Name
Abstract | ||
|---|---|---|
For the problem that traditional data association algorithms tend to coalesce neighboring tracks for multiple close targets tracking application in dense clutter, measurements adaptive censor (MAC) method to Set JPDA (SJPDA) algorithm was introduced in this paper, then the proposed the MACSJPDA algorithm of target tracking discards several data associations with small probability and accelerates the convergence speed of the SJPDA algorithm. The algorithm can achieve better effects of multiple targets tracking by multiple sensors in wireless sensor networks. Monte Carlo simulation revealed that estimation effect of the MACSJPDA algorithm is much smoother, and it needs less run time than SJPDA algorithm for handling closely spaced and crossing targets, in the meanwhile the mean optimal sub-pattern assignment (MOSPA) deviation of the MACSJPDA algorithm is also smaller. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s10586-018-1846-3 | Cluster Computing |
Keywords | DocType | Volume |
JPDA, Multiple targets tracking, Sensor networks, Target tracking | Journal | 22 |
Issue | ISSN | Citations |
6 | 1573-7543 | 0 |
PageRank | References | Authors |
0.34 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Biao Wang | 1 | 1 | 3.07 |
| Kelei Feng | 2 | 0 | 0.34 |
| Wenzhong Yang | 3 | 0 | 0.68 |
| Zhiyu Zhu | 4 | 0 | 0.34 |