Empowered by deep learning, data-driven smartphone inertial tracking approaches have attracted much attention due to high accuracy and robustness. However, training deep models requires significant amounts of IMU data with high-precision labels which incurs high annotation costs. In this work, we propose a practical data-driven IMU tracking solution without tedious annotations in a systematic way. We firstly design a simple but efficient method to annotate IMU data with phone labels based on the location service provided by smartphones. However, the phone labels are too noisy to train an accurate deep model. In-depth experimental studies reveal the noises are dependent along an IMU sequence; the noisy phone labels have knowledge useful for learning and make sense. The unique characteristics of the phone labels make existing learning-with-noisy-label models (LNLs) fail to work. We then propose EasyTrack, a noise-resistant data-driven IMU tracking system, which adopts a dual-model framework to enable LNLs. To handle the noisy labels, we design a series of effectively noise-resistant techniques. Extensive experiment results demonstrate without tedious annotations, EasyTrack achieves high accuracy by learning with noisy phone labels, outperforming existing LNLs and data-driven IMU tracking approaches.

Geomagnetic field and WiFi networks have attracted much attention in indoor localization, since they are pervasive, and their signals are easy to collect by off-the-shelf smartphones. However, there are still challenges in practice: 1) GFI (Geomagnetic Field intensity) read by magnetometer in smartphones is hard to be used directly, since it is in phone coordinate system and varies with phone attitude. On the other hand, the magnitude of GFI lacks location diversity; 2) WiFi RSS (Received Signal Strength) suffers from fluctuations due to multipath fading effect indoors. To address the above issues, we first propose Mag2D, a high quality feature of GFI. Mag2D is robust to phone attitude and has better spatial discrimination than the magnitude. We then study the complementary properties of Mag2D and RSS, and design MagWi, a practical fingerprinting indoor localization system fusing Mag2D and RSS. MagWi adjusts fusion weights of Mag2D and RSS according to their capabilities of discriminating locations. To do so, we propose LCD (Local Clustering Degree) to quantify the location-discriminating capability and build a LCD-Error model. The fusion weights are thus assigned dynamically based on the LCD-Error model during localization. We have implemented MagWi to provide location service on an Android phone in three typical indoor environments, covering a total area size over 600m2. Our experiment results show that MagWi achieves high accuracy and is easy to deploy in practice.