Indoor Positioning Using Multipath Components In Wireless Networks.
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This project aimed at solving a problem of how to relate maps in different coordinate system. This is done by using an algorithm that exploits multipath propagation for position estimation of mobile receivers. We apply an algorithm based on recursive Bayesian filtering named Channel SLAM. Consider a scenario where, at the same time that a user is walking, we estimate his position of the transmitter necessary to triangulate the user. With multipath we can position the user with a single physical transmitter, using Multipath Assisted Positioning (MAP), and with the help of Channel-SLAM (Simultaneous Localization and Mapping) the map of the virtual transmitter is estimated. Map uses multipath propagation produced by both reflection and scattering to estimate a point on the map where we can locate a virtual transmitter (VT), and treat each multipath components as if they were direct signals between the user and the estimated VT. Particle Filter (PF) which exploits information such as the angle of Arrival (AoA) or the time of arrival (ToA) of multipath components is used to position both the user and VT's. Channel-SLAM consider also paths occurring due to multiple numbers of reflections or scattering as well as a combination. The received multipath components increases the number of transmitters resulting into more accurate position estimate or enabling positioning as a number of physical transmitters is insufficient. Channel-SLAM estimates the receiver position and the positions of virtual transmitters simultaneously; hence the approach does not require any prior information, such as room layout or database for fingerprinting. The only prior information needed is the physical transmitter position as well as initial receiver position and moving direction