Indoor Postioning System(IPS) Using Mobile Device

Shiv Agarwal
January 24, 2013

Submitted as coursework for PH250, Stanford University, Spring 2012


The American Global Positioning System and its Russian cohort GLONASS have two fundamental flaws: They don't work indoors, and they only really operate in two dimensions. Now, these limitations are fair enough; we're talking about an extremely weak concrete and other solid obstacles is hard enough for a strong, short-range cellular signal - you can't seriously expect a 50-watt signal traveling 12,000 miles to do the same. Detecting a GPS signal on Earth is comparable to detecting the light from a 25-watt bulb from 10,000 miles. The situation is a little more complex when it comes to detecting a change in altitude; GPS and GLONASS can measure altitude, but generally the data is inaccurate and too low- resolution (on the order of 10-25 meters) for everyday use. Even with these limitations, though, space-based satellite navigation systems have changed almost every aspect of society, from hardware hacking to farming to cartography. What if we had a navigation system that worked indoors, though? What if we had an Indoor Positioning System (IPS)?

Deployment Initiatives

Last year, Google Maps for Android began introducing floor plans of shopping malls, airports, and other large commercial areas. Nokia, too, is working on an indoor positioning system, but using actual 3D models, rather than 2D floor plans. Just last week, Broadcom released a new chip (BCM4752) that supports indoor positioning systems, and which will soon find its way into smartphones.

Using WiFi for IPS

Successful localization and navigation is of utmost importance for task-driven indoor mobile robots. In the past, a variety of approaches to localization have been explored, including using sonar, laser scanners and vision. [1,2] With the increasing prevalence of wireless LAN, "WiFi," considerable work has been done on using signal strength measurements from WiFi access points for localization. Approaches based on wireless signal strength propagation models have been proposed, but due to the complex interactions of WiFi signals in particular in indoor environments, data-driven signal map based methods have been found more suitable. [3] Wifi-based localization has been shown to be successful in different scenarios in terms of its ability to support humans or robots to identify their locations. [4-6]

Using Bluetooth for IPS

Nokia's solution uses Bluetooth instead of WiFi, making it higher resolution (but it would require the installation of lots of Bluetooth "beacons"). Other methods being mooted involve infrared, and even acoustic analysis. None of these approaches are accurate or reliable enough on their own, though - in spaces that are packed with different materials, and roving groups of attenuating meatbags, these signals are simply too noisy.

Using Custom Chips

The Broadcom chip supports IPS through WiFi, Bluetooth, and even NFC. More importantly, though, the chip also ties in with other sensors, such as a phone's gyroscope, magnetometer, accelerometer, and altimeter. Acting like a glorified pedometer, this Broadcom chip could almost track your movements without wireless network triangulation. It simply has to take note of your entry point (via GPS), and then count your steps (accelerometer), direction (gyroscope), and altitude (altimeter).


In short, indoor positioning systems are coming - first to built-up and heavily-touristed areas and then, as smartphone saturation reaches 100%, everywhere else.

© Shiv Agarwal. The author grants permission to copy, distribute and display this work in unaltered form, with attribution to the author, for noncommercial purposes only. All other rights, including commercial rights, are reserved to the author.


[1] J. Biswas and M. Veloso, "Localization and Navigation for Autonomous Indoor Mobile Robots," 2010 IEEE Conf. on Robotics and Automation (ICRA), 4379 (2010).

[2] R. Sim and G. Dudek, "Learning Environmental Features for Pose Estimation," Image and Vision Computing 19, 733 (1999).

[3] M. Ocana et al., "Indoor Robot Localization System Using WiFi Signal Measure and Minimizing Calibration Effort," Proc. IEEE Intl. Symp. on Industrial Electronics 4, 1545 (2005).

[4] P. Bahl and V. Padmanabhan, "RADAR: An In-Building RF-Based User Location and Tracking System," IEEE Infocom 2000 (IEEE, 2000), p 775.

[5] Y. C. Chen et al., "Sensor-Assisted Wi-Fi Indoor Location System For Adapting to Environmental Dynamics," Proc 8th ACM Intl. Symp. on Modeling, Analysis and Simulation of Wireless and Mobile Systems, (Assn. Computing Machery, 2005), p 118.

[6] S. Zickler and M. Veloso, "RSS-Based Relative Localization and Tethering for Moving Robots in Unknown Environments," 2010 IEEE Intl. Conf. on Robotics and Automation (ICRA), 5466 (2010).