摘要
室内定位在公共安全、健康监护、定位服务等领域具有重要价值,提高定位精度和模型对环境的适应性已经成为室内定位的核心问题。其中通过接收信号强度指示RSSI值来获取距离是比较通用的方法。针对室内复杂环境中传统的对数距离损耗路径模型适用性不高的情况,提出了一种情境自适应的RSSI分段异构拟合定位方法。该方法利用信号在不同应用情境下传播损耗的差异性,将RSSI数据分为多个不同的拟合段,根据RSSI数据的区分特性寻找最优的分段拟合点,并为每个分段选择最优的拟合函数,使得分段数、分段位置和每个分段的拟合函数都适应相应的应用场景,从而实现高精度的RSSI信号拟合。实验结果表明,本文所提出的方法在RSSI拟合精度上均优于传统的单一拟合函数,可明显提高定位算法的精度。
Indoor positioning plays an important role in many applications, such as public safety, healthcare, location-based services and so on. How to improve positioning accuracy and the model＇ adaptivity to the environment becomes a key issue of indoor positioning, where most existing techniques generally use the value of the received signal strength indication （RSSI） to obtain the distance. Given the fact that the traditional logarithmic distance path loss model cannot adapt to the complex indoor environment very well, we propose a context-adaptive segmentation heterogeneous RSSI fitting positioning method. The proposed method firstly utilizes the difference in signal transmission under different application scenarios to divide the RSSI data into several different segments. Then it finds the optimal piecewise fitting point by RSSI＇s differentiated characteristics, and selects the optimum function for each segment, enabling the number of segments, segment position, and piecewise functions of all segments to adapt to corresponding application scenarios. Finally high accurate RSSI signal fitting is achieved. Experimental results show that the proposed method in RSSI fitting can achieve higher accuracy than the traditional single-fitting function, and improve the accuracy of position algorithms significantly.
出处
《计算机工程与科学》
CSCD
北大核心
2017年第7期1288-1294,共7页
Computer Engineering & Science
基金
国家自然科学基金(61173066
61572471
61472399)
广东省科技计划项目(2015B010105001)
中科院计算所所创新课题(20156010)
