Abstract
The IEEE 802.11mc-2016 protocol enables certified devices to obtain precise ranging information using time-of-flight-based techniques. The ranging error increases in indoor environments due to the multipath effect. Traditional methods utilize only the ranging measurements of the current location, thus limiting the abilities to reduce the influence of multipath problems. This article introduces a robust positioning method that leverages the constraints of multiple positioning nodes at different positions. We transfer a sequence of temporal ranging measurements into multiple virtual positioning clients (VPCs) in the spatial domain by considering their spatial constraints. Defining an objective function and the spatial constraints of the VPCs as Karush–Kuhn–Tucker conditions, we solve the positioning estimation with nonconvex optimization. We propose an iterative weight estimation method for the time of flight ranging and the VPC to optimize the positioning model. An extensive experimental campaign demonstrates that our proposal can remarkably improve the positioning accuracy in complex indoor environments.
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