# L5 IN: Overview of an Indoor Navigation Pilot Project
## 3 Implementation
### PDR Positioning Using 5G  
The combination of an accelerometer and a gyroscope is of particular interest for PDR. Often, these sensors are combined as three-axis sensors in a smartphone. The acceleration sensor registers the acceleration due to a gravity of 9.81 m/s\({}^{2}\) on its three axes. This allows a tilt calculation, but step detection is also based on the registered accelerations. The three-axis gyroscope registers angular velocities. With appropriate time measurement, the relative angles of rotation in space can be determined by integration. However, these inertial sensors are subject to high measurement uncertainties, which affect the position estimation by means of PDR. In particular, the integration of the rotation rates of the gyroscope leads to drifts in the determination of direction and, therefore, also in the position.  
There are numerous implementation examples for the realization of PDR. The basis of PDR is a combination of a pedometer, estimated stride length, and orientation. Once a step is detected, the step length estimation should start. One can have different step length over the walking time due to environmental constraints, such as user speed and terrain  
Figure 1: Components and structure of the indoor navigation application.  
conditions [31]. In this implementation, the step count and stride length are estimated by using acceleration data. The pedometer and the well-known k-based method, reproduced from [31] with K = 0.4, are used. The step orientation is calculated from the leveled gyroscope data by using the method in [32] (Figure 2).  
In PDR systems, it is necessary to solve the problem of inertial sensor drift. Additional constraints such as 5G reference points are needed to keep the error accumulation at a minimum. These constraints can not only determine the initial position and orientation of the device or the user, but can also provide information to keep the drift under control. The misalignment between user and the device should not be a problem any more, as the step detection and heading estimation methods can be accurately calibrated using 5G signals. In 3 GPP Release 17, the aim is to generate horizontal positioning accuracy of at least one meter and better for commercial use cases, like 0.2 m for some use cases [41]. Based on that, some of the reference points are considered as simulated 5G points. Within a post-processing try, the PDR points can be projected onto the 5G reference points based on the ratio of the distance and azimuth difference between the PDR and the 5G points. Such a projection needs to wait for a 5G signal, additional to the start point, to calculate the azimuth difference and correct the heading. The simulation below was partially inspired by using the reference coordinate and corresponding time discovered by a video analysis to see the very first performance of such an idea. The results present a promising output using the 5G test network (Figure 3).  
Figure 3: 5G simulation to correct the PDR coordinates: (**a**) implemented PDR using the abovementioned elements; (**b**) corrected PDR using the simulated 5G points.  
Figure 2: Realization of pedestrian dead reckoning (PDR) elements in the reproduction of available benchmarks: (**a**) step heading calculation from leveled gyroscope values; (**b**) K-based stride length estimation.