基于RSSI和辅助节点协作的Wi-Fi室内定位方法

随着Wi-Fi的广泛应用,基于RSSI的位置指纹信息的室内定位也越来越受到研究者的关注。针对传统的基于RSSI的指纹匹配定位中只利用单个节点信息的缺陷,提出了一种基于RSSI和辅助节点协作的Wi-Fi室内定位方法,该算法首先基于RSSI序列相似性选择合适的辅助节点,并测量节点间的距离作为辅助信息以提高定位精度,同时还采用了自适应有色噪声卡尔曼滤波减小室内复杂NLOS环境造成的TOF测距误差,最后通过建模搜索以得到节点的精确位置。实验表明,在复杂环境下该算法定位精度优于其他主流方法,适用于基于Wi-Fi的室内定位系统。

Wi-Fi indoor localization algorithm based on RSSI and assistant nodes collaboration

With the extensive development of Wi-Fi, indoor location services based on RSSI fingerprint have attracted more and more attention from researchers. Aiming at the fault of a single node used only in the traditional RSSI based fingerprinting algorithm, a Wi Fi indoor location method based on RSSI and assistant nodes is proposed. Firstly, appropriate assistant nodes based on the similarity of RSSI sequence are selected around the locating node and distances measured between nodes are used as auxiliary information to improve the positioning accuracy. Under the complicated indoor circumstance resulted by NLOS, adaptive Kalman filter with colored noise is used to reduce the error of TOF range. And finally the exact location of the node is acquired by the established searching model. The experiments prove that in complex environment the proposed algorithm applied in Wi Fi based indoor positioning system is better than other mainstream algorithm.