| Zhao Zhichao ( 赵志超),Rao Bin,Xiao Shunping,Wang Xuesong.[J].高技术通讯(英文),2012,18(2):139~144 |
|
| Heterogeneous sensors data fusion method based on peak picking in probability density space① |
| |
| DOI： |
| 中文关键词: |
| 英文关键词: data fusion, probabilistic grids, joint probability density matrix, localization, sensor network |
| 基金项目: |
| Author Name | Affiliation | | Zhao Zhichao ( 赵志超) | | | Rao Bin | | | Xiao Shunping | | | Wang Xuesong | |
|
| Hits: 860 |
| Download times: 0 |
| 中文摘要: |
| |
| 英文摘要: |
| The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix (JPDM) has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique. |
|
View Full Text
View/Add Comment Download reader |
| Close |
|
|
|
