Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning

Due to the emergence of various wireless sensing technologies, numerous positioning algorithms have been introduced in the literature, categorized into geometry-driven positioning (GP) and data-driven positioning (DP). These approaches have respective limitations, e.g., a non-line-of-sight issue for GP and the lack of a high-dimensional and labeled dataset for DP, which could be complemented by integrating both methods. To this end, this paper aims to introduce a novel principle called combinatorial data augmentation (CDA), a catalyst for the two approaches’ seamless integration. Specifically, GP-based data samples augmented from different positioning element combinations are called preliminary estimated locations (PELs), which can be used as high-dimensional inputs for DP. We confirm the CDA’s effectiveness from field experiments based on WiFi round-trip times (RTTs) and inertial measurement units (IMUs) by designing several CDA-based positioning algorithms. First, we show that CDA offers various metrics quantifying each PEL’s reliability, thereby extracting important PELs for WiFi RTT positioning. Second, CDA helps compute the observation error covariance matrix of a Kalman filter for fusing two position estimates derived by WiFi RTTs and IMUs. Third, we use the important PELs and the above position estimate as the corresponding input feature and the real-time label for fingerprint-based positioning as a representative DP algorithm. It provides accurate and reliable positioning results, with an average positioning error of 1.58 (m) and a standard deviation of 0.90 (m).

I. Introduction

With the widespread use of mobile devices, positioning a user has become an essential ingredient for location-based services, e.g., navigation and location-based gaming [2]. Due to the emergence of various wireless sensing technologies, numerous positioning algorithms have been developed, which can be broadly categorized into two approaches. One is geometry-driven positioning (GP), estimating a user’s location from the intersection among different measurements’ geometric representations. The other approach is data-driven positioning (DP), exploiting a dataset of various location-dependent features to infer a user’s location by matching a few key features extracted from the dataset. This paper aims to bridge the two via combinatorial data augmentation (CDA). Specifically, we augment a few measured data samples by applying GP to many data combinations, facilitating the use of DP by overcoming its practical limitations. We verify the CDA’s effectiveness by tackling various issues in WiFi positioning areas, such as the coexistence of line-of-sight (LoS) and non-LoS (NLoS) propagations, pedestrian dead reckoning (PDR), and fingerprint-based positioning (FBP).