UNILS: Unconstrained indoors localization scheme based on cooperative smartphones networking with onboard inertial, Bluetooth and GNSS devices
Location based services (LBS) are becoming important services on today's smartphones (SPs) tablets and wearable devices. Seamless outdoors indoors navigation, and especially for accurate indoors localization, is the main demand of LBS users. Onboard WiFi, Bluetooth (BT) or inertial sensors technologies have been proven to somewhat provide alternative solutions in GNSS signal denied areas (i.e. indoors) to define SPs location. However, limited coverage of WiFi access points (WAPs) pre installed BT anchors, constrained of WAPs/BT anchors physical positions within a building, and limitations of existing localization techniques (in a standalone mode) on SPs are some of the main challenges to designing a spontaneous autonomous positioning solution with reliable accuracy at reasonable cost. This paper proposes an unconstrained indoors localization scheme (UNILS) based on cooperative SPs networking to tackle these challenges. The aim of this new scheme is to fuse multi technologies measurements on SPs. The scheme uses relative pseudoranging (based on time of arrival `TOA' technique) approach between the connected SPs that are GNSS enabled, especially when the majority of the SPs are outdoors. Then the scheme combines this pseudoranges with uncertainty calculations from onboard dead reckoning (DR) measurements using Kalman Filter, that can provide seamless and improve location accuracy significantly, especially when deep indoors. This means, in deep indoors, UNILS can utilize only available devices/sensors on SPs, when communication with WAPs or BT anchors is considered unreliable or unavailable, to offer reasonable cost good localization performance. Results obtained from actual trials simulations (using OPNET) of this scheme (based on Android SPs network implementations for various indoors scenarios) show that around 3 meters accuracy can be achieved when locating SPs at various deep indoors situations.
2016 IEEE/ION Position, Location and Navigation Symposium (PLANS)