基于半监督流形学习的无线传感器网络定位方法及其应用

Localization algorithm based on semi-supervised manifold learning in wireless sensor networks and its application

提出了一种基于流形半监督学习的移动节点定位算法.该算法利用基于流形学习的半监督方法,通过一定量的有标签样本和无标签样本,获取隐含在节点接收信号强度信息中的流形结构,直接建立节点物理位置与接收信号强度之间的映射关系.算法不需要使用现有的理论或经验信号传播模型,避免了模型不准确带来的定位误差,而且允许网络中存在大量无标签样本,降低了数据采集难度,提高了算法实用性.冶金工业现场的实际应用结果表明,相对RADAR算法,本文算法具有较高的定位精度.

A localization algorithm based on semi-supervised manifold learning is proposed. Manifold structures hidden in the information of received signal strength can be obtained by the algorithm. It is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled samples and a large amount of unlabeled samples. Existing theories and experiential signal propagation models need not to be known in the algorithm,and localization errors generated by inaccurate models can be avoided. A number of unlabeled samples were used to decrease the difficulty of collecting data and increase the practicality of the algorithm. Real nodes were used to setup the network in metallurgical industry environments. Experimental results in metallurgical enterprises show that a higher accuracy with much less calibration effort is achieved in comparison with RADAR localization systems.