A novel motion-model-free UWB short-range positioning method

Abstract

In recent years, the number of location-based services is increasing and consequently, the researchers' attentions are captivated in designing accurate real-time positioning systems. Despite having a good performance in outdoor environment, global positioning system is not capable of estimating an object's position in an indoor environment precisely. In this paper, we present a novel tracking algorithm for indoor environment with a known floor plan. The object location is estimated by utilizing the information of the multipath components which are created by one physical and some virtual anchors. We will link this information to the floor plan by defining a channel model that has a combination of stochastic and deterministic traits. As we have used only one physical anchor in this paper, we would encounter several challenges such as lack of data association and existence of clutters amid real data. We dealt with these problems through random finite set methodology. Additionally, we will demonstrate that the proposed method is not restricted by the model of the motion and is capable to precisely track the trajectory. It will be shown that it provides a better accuracy, particularly in nonlinear trajectories, compared with two other relevant models which are adopting linear motion model.

Improving Indoor Localization Using Mobile UWB Sensor and Deep Neural Networks

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