The Contribution Of Deep Learning To Indoor Positioning Technologies

A combination of an indoor navigation solution and an indoor position system (IPS) opens up a whole new universe of possibilities.

FREMONT, CA: The majority of users have certainly encountered this situation before: they're within a large structure, such as a retail mall, event center, or underground parking garage and their navigation system is having difficulty locating them on the map. This is often caused by the concrete walls of the construction interfering with the GPS signal. Smartphone applications can give location-based information to users. They can use this information to seek driving directions, identify a retailer, or subscribe to alerts about nearby offers. GPS, which requires exposure to the outside for best accuracy, enables several valuable functions. They may, however, encounter challenges getting this data inside significant structures due to a weak GPS signal.

Accurate indoor positioning systems (IPS), utilizing public sensors and user consent, can deliver location-based information even when the user is not outside. An IPS is a network of devices used to identify persons or items in areas where GPS and other satellite technologies are insufficiently precise or fail altogether. This article provides an overview of the currently available IPS.

Bluetooth Low Energy technology (BLE) provides continuous location detection for continuous asset tracking with a room-accurate location. With Angle of Arrival (AoA), position calculation is substantially more precise but requires extensive sensor infrastructure and expensive technology. As a result of its low cost and ease of use, BLE and Beacons have become the preferred indoor location technology.

WiFi-based systems use WiFi transmitters as tags to transfer data to access points. Source location is calculated using information techniques. The data is stored in the cloud. They are accurate to within 3 to 5 meters using WiFi and time difference of arrival (TDOA) technology.

Three-dimensional positioning allows Ultra-Wideband (UWB) systems to attain excellent positioning accuracy. The ultra-broad UWB signal emits a vast pulse over a GHz spectrum, allowing continuous, highly accurate asset tracking. Historically, UWB-based systems have been the most precise. Despite the low cost of UWB tags, the limited range requires at least three readers. This makes UWB solutions costlier than BLE solutions.

New Deep Model Solutions

The data from these sensors can be enhanced using algorithms or deep models to improve speed estimate, noise reduction, zero-velocity detection, and altitude–location prediction. Speed estimate is a significant issue in the navigation sector. As estimation grows more precise, it affects the position solution as well. Low-cost sensors generate a lot of noise and have a noisy profile that fluctuates over time. Utilizing a suitable filter can assist in reducing noise. Due to the difficulty of estimating these noise profiles, deep learning algorithms can aid in assessing, predicting, and correcting these profiles.

IPS powered by technology can be applied to various industries, including manufacturing, retail, automotive, and field service. The accurate indoor positioning of assets within a building enables internal logistic processes and staff management optimization, making it a critical tool for increasing efficiency and lowering costs.