Along with the penetration of smart devices and mobile applications in our daily life, how to effectively manage the mobility issues in wireless networks becomes a challenging task. The ability to continuously and accurately track the target object’s position plays a vital role in mobility management. In this paper, we propose a novel indoor localization algorithm that fuses multiple signal features as the location fingerprints. The rationale that motivates our algorithm design stems from the following observation: although using one special signal feature (e.g., channel state information (CSI)) might achieve statistically higher accuracy than using another signal feature (e.g., received signal strength (RSS)), the accuracy for individual position estimations is usually diversified when only one signal feature is used in localization. For example, using RSS can obtain more accurate location estimation than using CSI for some individual positions. Thus, we propose a novel indoor localization algorithm that fuses multiple types of signal features as fingerprint of positions, which can effectively improve localization accuracy. We designed several fusion schemes and evaluated their performance. Experiments show that our algorithm achieves localization error below 0.5m and 1.1m in two typical indoor environments, about 30% lower than the accuracy of algorithms by fusing multiple signal features.
Localization algorithm design and performance analysis in probabilistic LOS/NLOS environment
