Introduction to Indoor Positioning

Introduction to Indoor Positioning

Imagine if you could go into a grocery store, take what you need, and walk out. In fact, you don’t need to imagine it; Amazon has already done this with their Amazon Go stores. This new form of retail is possible because of something called “indoor tracking” or “indoor positioning.” Indoor tracking applications range from smart retail (e.g. Amazon Go) to finding a package out of thousands in a warehouse to tracking cars in a parking garage. The overall benefits of indoor tracking are vast—lower operational costs, better coverage, arrangement optimization, etc. However, tracking assets indoors is application specific and quite difficult. Let’s go through some technologies that could help you implement an indoor positioning solution.

1. Global Positioning System (GPS)

Let’s first go over the Global Positioning System (GPS) technology. GPS has become ubiquitous over the past decade. Everyone with a smartphone has GPS at their fingertips, and consumers can buy GPS modules for less than $50. Many real-time IoT asset tracking applications are now possible with the widespread availability of GPS.

Nonetheless, GPS still struggles in one key application—indoor tracking. GPS technology struggles to track well indoors because GPS signals may not be able to penetrate through built structures. You can use an indoor gateway to boost signals and give more accurate positions, but GPS is generally inaccurate for indoor tracking applications.

2. Radio Frequency (RF)

The next set of technologies is radio frequency (RF) indoor tracking, which uses beacons to communicate via WiFi or Bluetooth signals depending on the application. WiFi and Bluetooth technologies use parameters such as the Received Signal Strength Indication (RSSI), Angle of Arrival (AoA), and Time of Arrival (ToA) to determine the location of a broadcasting device.

The RSSI value is inversely proportional to distance, so a device’s distance from a given radio beacon can be approximated. AoA describes the angle of a wave at radio beacon. ToA refers to the time at which a wave arrives at a beacon. AoA and ToA are difficult to determine indoors because radio travels at the speed of light.

Given the obstacles an indoor scenario presents, you often need multiple receivers for these measurements. WiFi and Bluetooth receivers aren’t typically built to deal with this level of precision, so RSSI is the primary metric used for RF indoor tracking.

3. Ultrasonic

Ultrasonic sensors could be used in a sensor array to figure out where an asset is in an indoor space. Much like RF, we can use the sound waves to determine the RSSI, AoA, and ToA of a device. However, it’s much easier to determine distance because sound travels much slower than light, so the calculated distance using the ToA is typically much more accurate than RF. However, ultrasonic sensors are very limited in terms of the width of the beam pattern, which means they can detect assets far away, but neither too far left nor right of the ultrasonic beam.

4. Computer Vision

Indoor tracking uses computer vision much like any other computer vision application. A computer vision algorithm will identify the asset that needs to be tracked and follow it as it moves through the environment. This is part of the technology that Amazon uses in its Amazon Go stores to track customers as well as items for sale. However, this technology is very use case specific. For an RF application, it doesn’t matter whether a person or your pet dog is wearing the tracker. However, this makes a huge difference for a computer vision system.

5. Algorithms & Tuning

There are numerous computer vision algorithms, so I’ll just go over algorithms that are relevant to RF and ultrasonic indoor tracking technologies. Trilateration and triangulation are mathematical algorithms that use three beacons to determine the location of an asset. Trilateration and triangulation use distance and angles, respectively. These algorithms aren’t foolproof because RF and sonic waves don’t travel unobstructed to beacons. To account for obstruction, we can use empirical models to consider the time of day, number of assets, etc., to determine location more accurately. However, even using those aiding data, indoor positioning remains difficult. RF is only accurate to two or three meters—a significant area in a small office space, grocery store, or retail environment.

The final step here is to combine multiple sensor types using a technique called “sensor fusion.” An example of sensor fusion is using a BLE tag with an accelerometer and gyroscope. The accelerometer and gyroscope can give you the general movement and direction of a given asset, which can help you track it with RF technologies. With empirical models, you can get a better idea of an asset’s location compared to traditional RF tracking.

Limitations

Indoor tracking is quite difficult for two reasons: technology isn’t extremely accurate, and the environment is constantly changing. With both of those factors influencing outcomes, indoor tracking solutions can run awry quickly. RF tracking is accurate up to a few meters, however, some indoor spaces are only a few meters. In contrast, ultrasonic tracking is much more accurate, but the width of its beam pattern is quite constrained. Sensor fusion provides one promising solution by leveraging multiple sensor inputs to get more accurate data.

Indoor environments often change. RF and ultrasonic waves can absorb and reflect off of various surfaces, which will change key characteristics of these waves and, therefore, the derived calculations—RSSI, AoA, and ToA. In a static environment, you can create an empirical model that accounts for unique scenarios. However, we generally want to track people and things that are moving. As a result, the surfaces move and change. The empirical model you use must be more sophisticated and multivariate to deal with these changes.

Conclusion

There are numerous technologies such as WiFi, Bluetooth Low-Energy (BLE), Ultrasonic, Computer Vision, etc., which can be deployed to track assets indoors. However, indoor tracking is difficult because of accuracy limitations due to the underlying technology and because of shifting indoor environments. Methods such as empirical models and sensor fusion can be used to account for these issues, yet robust and flexible deployments remain a huge challenge.