An opportunistic indoors positioning scheme based on estimated positions

Positioning requirements for mobile nodes in wireless (sensor) networks are largely increasing. However for indoors environments most of the existing solutions are expensive in terms of technology or infrastructure. We propose an indoors positioning scheme in which a static user collects self-estimated positions of collaborating peer nodes within radio range as well as the error ranges of these estimations and determines its position with a linear matrix inequality (LMI) method coupled with a barycenter computation. The approach is evaluated with simulations where the input parameters are the maximum radio range, the number of peer nodes within range, their speed, and dynamic error estimation. The simulations show that the scheme works fine even when the error estimation linearly increases with time. To estimate its position within 1 meter accuracy, the user node may wait at the same position for 5 minutes, with 16 pedestrians moving around him and an error increase rate of 10%.