Overview of indoor navigation technologies

       Development of indoor navigation services and algorithms is becoming a popular trend in IT-industry in recent years. Some of the modern buildings, like airports, shopping malls, warehouses have grown enough (Fig.1) to feel a need for their own navigation tools for customers. Closed environment conditions exclude the usage of common satellite-based navigation systems like GPS or GLONASS, so nowadays some alternative information sources of user localization appear at the scene.


Fig.1. Amazon warehouse

The idea of tracking some object inside the closed space is not completely new. Modern Indoor navigation systems (INS) could implement different physical principles, and provide an accuracy of location from dozens of meters to centimeters. Their typical operation area or footprint, complexity, and cost may differ at tens of times. Besides the location, indoor navigation systems (INS) are potentially able to provide some other related services, like an optimal routing, tracking of the most popular places that people attend, sending a notification when the user reaches a point of interest etc [1]. Let us talk about some of them.

Let us first give an overview of approaches to Indoor positioning and navigation before we start the comparing them highlighting their pros and cons. All known methods of INS may be put into two baskets:

1) Measuring the distances to some reference points;

2) Identifying some unique tags, or fingerprints. It could be one parameter (for example, serial ID) or a combination of some parameters (for example, a matrix of reference signal measurements).

Some INS track the distances between the target (user) and some reference points (beacons). This is known as trilateration and its more common case – multilateration [2]. Other INS identify some unique fingerprint to find the user position directly [3], see Fig. 2.


Fig. 2. Distance based and Fingerprint based types of INS

There are some options (technologies) to build a local indoor navigation system:


The first candidate to provide information to user location indoor service is data collected by the LTE Direct technology. LTE-Direct is a global standard to a device to device discovering. Every device is able to broadcast an expression in periodic session (see Fig. 3), e.g. every 20 sec. An expression is a 128-bit service layer identifier. It can represent an identity, a service, an interest, a location etc. The LTE-Direct device receives thousands of expressions from other devices around (up to 500 meters). More data about the subject could be loaded from the special Expression Name Server (ENS) through the link (like URL) in the broadcasted messages.


Fig. 3. LTE-Direct communication

Pros & Cons




  • Reduced power consumption (in comparison with other cellular network technologies because of excluding intermediate nodes like base stations).
  • Wider discovery range against Wi-Fi and Bluetooth.
  • Too high power consumption for autonomous INS.
  • Needs external power supply.
  • Low accuracy (hundreds of meters). Some experts talk about 50 meters in the future.

Conclusion: It could be good for emergency calls to help locate the injured person, but it is also inconvenient for regular people tasks like locating the exit from the building.

Wi-Fi access points (AP) could be also considered as useful and promising navigation data sources. Almost no additional hardware is needed for such technology of user position displaying. Only appropriate placement of existing Wi-Fi access points (taking into account their minimal amount for navigation algorithm) and correspondent software are needed. In order to measure the distance from the user device to Wi-Fi AP, the Received Signal Strength Indicator could be used, Fig. 4.


Fig. 4. Wi-Fi based navigation

RSSI is the power on the receiver’s side (mobile phone or tablet) from different Wi-Fi APs. Knowing the initial power of transmitted packets and calibration relation “power vs distance” it is possible to calculate the ranges to each AP.

Pros & Cons



  • No additional hardware is needed.
  • Can use mobile phones and tablets as navigation terminals.
  • Peculiarities of Wi-Fi signals propagation would have led to low accuracy in navigation (about dozens of meters).
  • There are strong dependencies on the external power supply and communication channels.
  • No one could deny the AP owners to move their Wi-Fi access point to some other place in future.

Conclusion: Wi-Fi data could be used for references in the case when other sources are unavailable.

More about this technology you can read here, here and here.

RFID navigation systems are built on the base of RFID tags, Fig. 5. The tags are passive small and cheap devices, that could be powered by the induced electric field, and after activation will transmit previously recorded information. More about this you can read here.


a) RFID tag.                        b) RFID-based INS operation.

Fig. 5. RFID technology

Pros & Cons



  • Pretty good accuracy (about 1 m).
  • Cheap.
  • Simple system installing and maintenance.
  • RFID reader is needed.
  • Hardly suitable for human navigation with arbitrary user motion.

Conclusion: possibly good for loader trucks and some other vehicles (Fig. 5) in an industrial environment and in the case of several predefined routes.

Geomagnetic positioning is one more promising technology. For years’ people have been talking about positioning with the help of local disturbances of Earth magnetic field (Fig. 7), there are some prototypes available, but it is still hard to find now the working cheap geomagnetic INS.

 fig6a  Fig6b
a) Earth geomagnetic field disturbances could be used for indoor navigation. b) Geomagnetic positioning principle demonstration.

Fig. 6.  Geomagnetic positioning

The issues of implementation are the permanent change of the Earth magnetic field, perturbations caused by electric wires inside the building etc. 

Pros & Cons




  • Can use mobile phones and tablets as navigation terminals.
  • Unstable accuracy. Measurements can be distorted by the electric wires, changing of the Earth magnetic field

Conclusion: promising technology, but still in the experimental phase.

Ultra Wide Band (UWB) indoor navigation systems use the very useful feature of the UWB signals easy penetrate through the walls, human bodies, and other obstacles. Therefore, unlike the narrow-band reference signals INS (Wi-Fi or Bluetooth), the UWB navigation signal, that uses a range of frequencies from several hundred MHz to several GHz requires less number of beacons to cover some area inside the building. Especially when there are a lot of obstacles inside. Measuring the phase of the receiving UWB signal one could determine a distance from high (up to several centimeters) accuracy. There are some interesting videos about UWB INS are available here and here.

One of the examples of discussed system is KIO RTLS (Real Time Location System), with UWB anchors, see Fig. 7.


Fig. 7. UWB beacons

Pros & Cons



  • High accuracy (tens centimeters).
  • UWB INS are not sensitive to walls, human bodies, and other obstacles.
  • Require special equipment.
  • High cost

Conclusion: UWB INS belong to professional equipment. They found their use for some critical services, for example, tracking the service personnel or important medical equipment in the hospitals.

More details about UWB are available here.

Ultrasonic INS demonstrate accuracy about several centimeters. Ultrasonic systems also could use trilateration principle, like Wi-Fi INS do, but the main advantage is a lower signal propagation speed. For instance, the sonic velocity in the standard day atmosphere is about 343 meters per second. One could compare it with 300 000 000 meters per a second for radio waves. The lower wave propagation speed awards in higher localization accuracy.  Thus, the distance between the target (ultrasonic receiver) and reference points could be evaluated at low error level(1-2 cm).


Fig. 8. Receiver in the ultrasonic  INS

One of the known implementation of the ultrasonic INS uses GPS-like coordinate measurement algorithm, that is scaled to a single room environment. Ultrasonic Beacons, like satellites, give a reference signals to the receiver. Arrival time defines the delay and, correspondently, the distance. Satellites use ultra-precision atomic clocks, and ultrasonic beacons use the special router to synchronize the time through the radio channel. Using multilateration principle, (see Fig. 2), it is obvious how to convert measured distances to user coordinates inside the building. 

Pros & Cons




  • High accuracy (about tens of centimeters).
  • UWB INS are insensible to walls, human bodies, and other obstacles.
  • Special equipment is needed, see Fig. 8.
  • Limited range (possibly 10-50 meters).
  • The line of sight requirement must be fulfilled.
  • Accuracy depends on air temperature fluctuations.

Conclusion: ultrasonic systems are more suitable for navigation of mobile robots (see Fig. 9) in the medium sized areas (hundreds of square meters).


Fig. 9. Mobile robot with ultrasonic navigation

More information follows here and here.

Bluetooth Low Energy (BLE) beacons INS seems very promising technology. It supposes only one-way communication between the small radio transmitting devices (Beacons) and mobile devices. Hence, the BLE devices require only small batteries to operate for 2-3 years.


Fig. 10. Bluetooth beacons technology components

Fig. 10 shows some main components of the Beacons. More details about Bluetooth Beacon hardware you can read here. There are a big variety of Beacons shapes and models, see Fig. 11.


Fig. 11. BLE Beacon devices

iBeacon standard uses Bluetooth 4 Low Energy standard packets for broadcasting of navigation information, see Fig.12.


Fig. 12. iBeacon data structure

BLE beacons were initially started in 2013 when Apple introduced the iBeacon standard. This standard is proprietary, allows only one type of advertisement packet, which consists of the following parts (Fig. 11). Next was AltBeacon standard. It was created as an open alternative of the iBecon, and designed to be compatible with it. In 2015 Google has introduced Eddystone standard. Unlike iBeacon and AltBeacon, it supports 3 types of packages: Eddystone-UID (user ID), Eddystone-URL,  Eddystone-TLM (telemetry).

Initially, it was designed to use Bluetooth beacons only for the raw user localization and send push notifications when user reach some “checkpoint”. But further development of this technology has unchained its potential. The precision of user localization has significantly increased. Many companies have developed their own SDK (software development kit) to develop mobile applications for user navigation with Bluetooth beacons (example 1, example 2, example 3). In table 1 a brief overview of existing SDK and their qualities have been done.

Table 1: Comparison of popular existing indoor SDK’s

tableSDKPros & Cons




  • Enough accurate navigation (up to 1 meter).
  • Can use mobile phones and tablets as navigation terminals.
  • Cheap.
  • Simple system installing and maintenance.
  • Complex signal processing is needed to reach good accuracy.

Conclusion: one of the most universal and promising techniques to build INS.

Table 2 consist of summary about different INS. Price on the BLE beacons starts with 10-15 USD, which is definitely the lowest in our overview.

Table 2: Indoor navigation data sources


Concluding all the review BLE beacons seem to be more universal, cheap and promising technology in the future of INS systems. Interested readers can look at below references.


[1]    Ievgen Gorovyi, Alexey Roenko, Alexander Pitertsev, Ievgen Chervonyak, Vitalii Vovk, “Real-Time System for Indoor User Localization and Navigation using Bluetooth Beacons”, Proc. of the 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kiev, Ukraine, 2017.

[2]      A. Norrdine, “An Algebraic Solution to the Multilateration Problem”, Proc. of the 3rd Internat. Conf. “Indoor Positioning and Indoor Navigation”, Sydney, Australia, 2012.

[3]      M. Kaustinen, M. Taskinen, T. Säntti, J. Arvo, T. Lehtonen, “Map Matching by Using Inertial Sensors”, Literature Review of University of Turku Technical Reports  No. 6,  Turku,  Finland, 2015.

[4]    A. Thiagarajan, “Probabilistic, “Models For Mobile Phone Trajectory Estimation”, Thesis of master degree of Doctor of Philosophy in Computer Science and Engineering, Massachusetts Institute Of Technology,  Cambridge, USA, 2011.

[5]       S.F. Persa, “Sensor Fusion in Head Pose Tracking for Augmented Reality”, Thesis of master degree in Delft University of Technology, Delft, Netherlands, 2006.


Overview of indoor navigation technologies

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