A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors

This paper presents a hybrid pedestrian navigation algorithm based on investigation of different combinations of pedestrian dead-reckoning (PDR), WiFi fingerprinting, and magnetic matching (MM). A multilevel quality-control mechanism is developed based on the interaction between different techniques. The algorithms were evaluated by walking in two indoor environments, with two smartphones, and under four motion conditions (i.e., handheld, at an ear, dangling with hand, and in a pants pocket). It was found that 2D accuracy of WiFi fingerprinting and MM is related with received signal strength and magnetic distribution, respectively. MM results had small errors on some occasions but suffered from significant mismatches. WiFi-aided MM provided better results than either WiFi or MM, but still had a risk of mismatching. Furthermore, integration of PDR, WiFi, and MM reduced dependency on both navigation environment and motion condition. The proposed algorithm provided more reliable solutions than both PDR/WiFi and PDR/MM, especially in areas with poor WiFi signal distribution or indistinctive magnetic features.