Abstract: Most of the traditional positioning methods in wireless sensor network mainly study the matching relationship between the Received Signal Strength（RSS） and do not give full consideration to the physical environment and the use of off-line training database information. As to the above problem, this paper puts forward a new positioning method based on Gaussian fitting of deep similar domain. This method establishes a mapping relationship between distance of RSS in the offline phase, and uses the information of the node which to be located and its surrounding physical neighbor points in the domain of logic deep similarity. At the same time, it uses the methods of Bayesian algorithm and Gauss curve fitting to get the measured values of RSS. It can improve the accuracy of the fingerprint locationing method. Experimental results show that the method improves the precision of positioning effectively.

Key words: sensor network, received signal strength, Bayes, Gaussian fitting

摘要： 传感器网络中大多数传统定位方法主要研究接收信号强度（RSS）之间的匹配关系，而未充分考虑物理环境和利用离线训练数据库信息。针对上述问题提出一种基于RSSI深相似域高斯拟合的定位方法。该方法在离线阶段建立RSS与距离之间的映射关系，并利用待测定位节点与其逻辑最近邻周边深相似域内的物理近邻点的信息，同时使用贝叶斯算法和高斯曲线拟合来获取参考节点RSS的测量值，最终提高指纹定位方法的准确性。实验结果表明，该方法有效地提高了定位的精度。

关键词: 传感器网络, 接收信号强度, 贝叶斯, 高斯拟合