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The advent of sensor-rich smart devices (e.g., smartphones) has enabled a lot of applications and services. One of these applications and services is smartphone-based vehicle indoor positioning, which is a key technology for smart car parking and driverless cars. So far, most vehicle indoor positioning solutions either use infrastructures (e.g., WiFi access points) or inertial sensors, which suffer from low positioning accuracy, limited coverage, or high cost to deploy new equipment. To tackle these challenges, in this work we propose a novel Deep Learning-based Vehicle Indoor Positioning (Dee...

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is nontrivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and users orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, user moving direction or user orientation in a give...

Share: Researchers Propose a High-Accuracy Approach for Indoor Location Detection, With Various Potential Applications in Future ‘Smart' Environments CINCINNATI, OH / ACCESSWIRE / April 1, 2021 / While GPS can help us successfully navigate outdoors, detection of indoor locations remains a challenge and needs to be addressed to enable age-friendly smart environments for the rapidly increasing aging population of the world. In a new study, researchers from the US take indoor location detection to the next level through their proposed artificial intelligence and machine learning-based approach...