Currently Available Indoor Positioning Systems Technologies

Accurate indoor positioning systems can provide location-based information even when one is not outside, based on public sensors and user permission.

FREMONT, CA: An indoor positioning system (IPS) is a network of devices used to locate people or objects in situations where GPS and other satellite technologies are ineffective. Examples are multistory buildings, airports, alleys, parking garages, and underground locations. Unfortunately, current IPS solutions are inaccurate, particularly in multistory buildings.

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In Brief: Indoor Positioning Technologies

Bluetooth Low Energy (BLE) technology can detect a person's or object's general location, allowing for continuous asset tracking with at least room-accurate location via BLE sensors/beacons. On the other hand, position calculation using the Angle of Arrival (AoA) provides more precise localizations, requiring a large sensor infrastructure and hardware investment. Therefore, due to low cost and ease of use, Bluetooth Low Energy and Beacons have emerged as the ideal indoor location technology.

WiFi-based systems employ WiFi transmitters as tags to send data to multiple WiFi access points. Information algorithms use these readings to calculate the location of the source. The location data is eventually transmitted to a cloud environment. Systems based on WiFi and 'time difference of arrival (TDOA) technologies provide a relatively high level of accuracy (within 3 to 5 meters), but these systems can be costly.

UWB systems achieve extremely high accuracy through three-dimensional positioning. Because of the extremely wide UWB signal and the ability to transmit a very wide pulse over a GHz spectrum, continuous, highly accurate asset tracking is possible. Historically, UWB-based systems have achieved the highest accuracy. However, even though UWB tags are inexpensive, due to the confined ranges of the tags, every location must have at least three readers. As a result, UWB solutions are significantly more expensive than BLE solutions.

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New Deep Model Solutions

Data from these sensors can be optimized using algorithms or deep models to improve speed estimation, noise reduction, zero-velocity detection, and altitude–position prediction. Speed estimation is one of the most difficult problems in navigation. The position solution will change as the estimate becomes more accurate. Low-cost sensors suffer from high noise levels and have a noise profile that changes over time. The use of an appropriate filter can aid in noise reduction. Because these noise profiles are difficult to estimate, deep learning algorithms can assist in analyzing, predicting, and correcting them.

Manufacturing, retail, automotive, field service management and other industries can benefit from technology-driven IPS. In addition, the precise indoor positioning of assets in a building allows for the optimization of internal logistic processes and staff management, making it a valuable tool for increasing efficiency while lowering costs.