# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 1 Introduction
Although there is no definitive metric or a well-defined and agreed upon grouping performance thresholds [17, 18], in general, there are four grades--in some cases five--of IMUs that are based on grouping of likewise performance and main applications. A comparison of the different types and grades of IMUs is presented in [2]. The comparison shows the relatively high errors of the MEMS IMUs--also referred to as commercial grade--in comparison to the higher more expensive counterparts, where the performance charts show that MEMS technology suffers from the highest error budget in comparison to other technologies. Since DR is a relative navigation technique, and since MEMS IMU suffer from high errors, the navigation solution drifts quickly from the true values of position and attitude. Hence, DR requires correction techniques or even aiding from other technologies to provide corrections for the navigation states. Methods for correction have been developed and used to enhance the performance of DR. Some of these correction techniques include: zero velocity update (ZUPT) [19], zero angular rate update (ZARU) [20], magnetic angular rate update (MARU) [21], and heuristic drift reduction (HDR) [22]. These techniques exploit certain motion constraints to provide corrections, but in the case of smartphone-based navigation, many of these conditions do not occur frequently. Pedestrian dead reckoning (PDR) is a modified form of DR that was developed especially for pedestrian navigation and uses human motion information--namely the gait cycle (GC)--to improve the performance over regular DR methods. PDR consists of four main parts: attitude estimation, step detection, heading estimation, and step length estimation. This research presents a modified PDR system for smartphone navigation that exploits tendencies in human motion to further reduce the error accumulation in the navigation states. The developed system can: accurately detect steps under varying motion speeds and smartphone use cases, provide frequent attitude corrections without imposing motion restrictions on the user, and estimate motion direction with high accuracy. The contributions of this system are: (1) accurately detect steps using sensor fusion while being able to verify steps taken and compensate for missed steps, (2) provide attitude corrections as frequent as a step-by-step basis without imposing motion constraints on the user and without relying on previous attitude states to eliminate drift, (3) estimate the direction of motion of the user with high accuracy by analyzing the accelerations within a step and eliminating outliers to obtain a reduced data set that accurately captures the direction of motion of the user. This article is organized as follows: After covering some of the state-of-the-art PDR systems and the innovations in the PDR modules in Section 2, the developed method for PDR and the underlying enhancements in each of the main PDR subsystems is presented in Section 3. In Section 4, a description of the testing and experimental setup is provided, followed by performance analysis, evaluation, and discussion in Section 5. Finally, conclusions are presented in Section 6.