Transceiver-free object localization can localize target through using Radio Frequency(RF) technologies without carrying any device, which attracts many researchers' attentions. Most traditional technologies usually first deploy a number of reference nodes which are able to communicate with each other, then select only some wireless links, whose signals are affected the most by the transceiver-free target, to estimate the target position. However, such traditional technologies adopt an ideal model for the target, the other link information and environment interference behavior are not considered comprehensively. In order to overcome this drawback, we propose a method which is able to precisely estimate the transceiver-free target position. It not only can leverage more link information, but also take environmental interference into account. Two algorithms are proposed in our system, one is Best K-Nearest Neighbor(KNN) algorithm, the other is Support Vector Regression(SVR) algorithm. Our experiments are based on Telos B sensor nodes and performed in different complex lab areas which have many different furniture and equipment. The experiment results show that the average localization error is round 1.1m. Compared with traditional methods, the localization accuracy is increased nearly two times.
在真实室内环境中,用MICA2节点设计分析影响无线接收信号强度(radio signal strength,RSS)的实验,发现其影响因素不仅包括发送接收方(transmitter-receiver,T-R)之间的距离,且MICA2节点的工作频率和供电电池电压、发送接收方节点差异、天线角度和高度,以及环境中的时空因素和动态环境等都会影响无线接收信号强度.经分别测试这些因素,建议传统无线信号传播模型和信号校准算法应综合考虑各项影响因素.
