一种改进的基于信号强度的WLAN定位方法

与室外定位技术相比(如GPS),基于无线局域网(Wireless Area Network)的定位更适用于室内环境。两种基于接收信号强度(RSSI,Received Signal Strength Indication)的定位方法即位置指纹法和信号传播模型法广泛用于室内定位的研究。使用边界盒算法和改进的二分范围搜索算法将两种室内定位方法相结合,提出了一种改进的基于RSSI的定位方法。提出的方法根据指纹数据库中样本的横纵坐标对其进行预处理,同时使用改进的线性二分范围搜索算法降低指纹数据库中的样本数,进而提高实时定位过程的效率。最大化位置指纹维度,并添加时间维,同时通过实验数据阐明所提出的方法可提高用户位置估算的精确度。     

A smartphone-based multimodal indoor tracking system

The great popularity of smartphones, together with the increasingly important aim of providing context-aware services, has spurred interest in developing indoor tracking systems. Accurate tracking and localization systems are seen as key services for most context-aware applications. Research projects making use of radio signals detected by radio interfaces and the data captured by sensors commonly integrated in most smartphones have already shown promising and better results than location solutions based on a single data source. In this paper, we present a multi-sensor tracking system built by incrementally integrating state-of-the-art models of the Wi-Fi interface and the accelerometer, gyroscope and magnetometer sensors of a smartphone. Our proposal consists of a simple calibration phase of the tracking system, which involves enabling simultaneous data gathering from all three sensors and the Wi-Fi interface. Taking the Wi-Fi signal model as baseline, four different configurations are evaluated by incrementally adding and integrating the models of the other three sensors. The experimental results reveal a mean error accuracy of 60 cm in the case when the tracking system makes use of all four data sources. Our results also include a spatial characterization of the accuracy and processing power requirements of the proposed solution. Our main findings demonstrate the feasibility of developing accurate localization indoor tracking systems using current smartphones without the need for additional hardware.