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摘要: 针对大规模传感器网络(Large-scale sensor networks)的目标跟踪问题, 本文在贝叶斯(Bayes)框架下, 提出了一种全新的基于传感器选择的多传感器目标跟踪算法.算法的具体思路为:首先基于Bayes框架, 根据不同的管理目标, 推导出传感器选择的目标函数; 然后根据目标函数, 计算出相应的传感器选择方案; 最后将选择的传感器进行数据融合, 求得传感器网络的目标跟踪结果.相比传统的基于量测野值点剔除思想的目标跟踪算法以及基于系统偏差估计的传感器配准算法, 本文提出的基于传感器选择的多传感器目标跟踪算法不仅目标跟踪精度更高, 且跟踪性能更稳定.同时本文提出的传感器选择算法还可以适用于杂波数目较少的目标跟踪场景.仿真结果说明了本文所提算法的有效性.Abstract: In large-scale sensor networks, a target tracking approach based on sensor selection is presented in the Bayes framework.The proposed approach mainly contains the following three steps.Firstly, the objective function is obtained in the Bayes framework.Then, the sensor selection strategy is adopted according to the objective function.Finally, tracking results are obtained by fusion of those selected sensors.Compared with the traditional target tracking approach, the proposed sensor selection approach is much easier and more reliable.Moreover, the clustered target tracking scenarios are considered in this research, thus so the proposed approach is robust for target tracking applications.Simulation results show the effectiveness of the proposed approach.
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Key words:
- Large-scale sensor network /
- Bayes framework /
- sensor selection /
- target tracking /
- information fusion
1) 本文责任编委 潘泉 -
图 4 位置估计RMSE结果的对数值随时间的变化曲线
Fig. 4 The logic RMSE results on target position via sampling time
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