What are the challenges in the design of smartphone indoor positioning systems?

Magnetic anomalies can be seen everywhere. Uncertainty in orientation is the main cause of positioning errors. Although the use of magnetometers can avoid the "heading drift" problem that occurs during orientation, smartphones typically experience geomagnetic anomalies 60% of the day. This makes the smartphone indoor positioning system face no small challenge, can overcome this challenge?

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Over the years, indoor positioning systems using pedestrian dead reckoning (PDR) technology have received widespread attention in both academic and commercial fields. The various sensor solutions available today typically use an accelerometer to calculate the number of steps and use a magnetometer and/or gyroscope to measure changes in the direction of travel. The measurement accuracy is between 0.5% and 10% of the travel distance. But all of these methods require the user to maintain a balance from start to finish to ensure that the motion-sensing device is smooth, just like walking while maintaining the balance of a piece of cake, the so-called "cake step."

But the smartphone's indoor positioning system should allow users to move freely and provide reasonable results no matter how the phone is placed. Indoor positioning by developing sensor algorithms is extremely complex, in part because algorithms are subject to changes in the following factors, and as the environment changes in real time, the system must also take into account these factors.

Geomagnetic anomalies can be seen everywhere. Uncertainty in orientation is the main cause of positioning errors. Although the use of magnetometers can avoid the "heading drift" problem that occurs during orientation, smartphones typically experience geomagnetic anomalies 60% of the day. As shown in Fig. 1, when the mobile phone is smoothly held by an ordinary electric pole, it can be seen that the heading is swung and becomes extremely inaccurate. Through the careful design of the algorithm, these anomalies can be detected and compensated to make the orientation more precise (as shown by the blue line in the figure).

Galaxy SIII Walking Past Electrical Pole: Heading offset of the Galaxy SIII through the pole

Galaxy SIII Walking Past Electrical Pole: Heading offset of the Galaxy SIII through the pole

Figure 1. When passing an electromagnetic interference source (such as a utility pole), the orientation function of an ordinary default Android phone will become poor (red line). After implanting and installing Sensor Platforms' FreeMotion Library on the same phone, the orientation function became accurate (blue line).

Yaw (deg): heading offset (unit: degree)

Time (sec): time (in seconds)

Consumer-grade inertial sensors in smartphones are noisy and unstable. In some academic articles, the inertial measurement unit (IMU), which has an accelerometer noise of 1 mg and a gyroscope offset drift of 20 degrees per hour (far from the military-grade sensor), is called a low-quality IMU. However, even a smartphone The best sensor in the world will also generate one or two orders of magnitude more noise than this value. Therefore, this noise accumulation can quickly lead to serious positioning errors. Before improving sensor hardware performance, some algorithms need to be introduced to reduce dead reckoning errors, such as using PDR technology to calculate the number of steps.

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