Geometric Approach in Simultaneous Context Inference, Localization and Mapping using mm-Wave

Abstract

This paper introduces a combination between Angle-of-Arrival (AoA) and Time of Arrival (ToA) for localization and mapping in an indoor environment with single static receiver and N access points (APs) with millimeter wave (MMW) propagation characteristics. Using Received Signal Strength (RSS), the paper also proposes an approach for obstacle localization, mapping and classification. The latter is done by firstly estimating the positions of virtual anchor nodes (VANs), known as mirrors of the real anchor with respect to obstacle. Then, the obstacle position and dimensions are found via the estimation of the reflector points on the obstacle. Using Snell's law and the relation between RSS and reflection coefficient, different obstacles can be classified as per their material composition. Simulation results have shown the accuracy of the proposed approach in context inference and mapping. The work here will open the door for multiple applications in robotics, health, radar-like systems, and Internet of Things (IoT).