Multipath assisted positioning using machine learning

The multipath propagation of the radio signal was considered a problem for positioning systems that had to be eliminated. However, a groundbreaking new approach called multipath assisted positioning caused a paradigm shift, where multipath propagation improves the positioning performance. Moreover, the multipath assisted positioning algorithm called Channel-SLAM shows the possibility of using a single physical transmitter in a multipath environment for positioning. In this thesis, I open a discussion on some problems that have vital importance for multipath assisted positioning algorithms with a focus on pedestrian positioning. Using the idea of multipath assisted positioning, I present a single frequency network positioning algorithm. I evaluated the single frequency network-based positioning algorithm for positioning in a real scenario using a terrestrial digital video broadcasting transmission. I propose a novel pedestrian transition model utilizing the inertial measurements from a handheld inertial measurement unit. The proposed pedestrian transition model improves the precision and reliability of the Channel-SLAM. Comparing the proposed transition model with the Rician transition model previously used in Channel-SLAM quantifies the performance improvement. This thesis proposes a joint data association technique that overcomes the strong dependence on the radio channel estimation algorithm used in Channel-SLAM. The joint data association allows reusing the previously observed virtual transmitters after an outage of multipath component tracking. The evaluation based on the walking pedestrian scenario shows that the joint data association algorithm provides superior positioning precision. The virtual transmitter position estimation yields a significant computational load in Channel-SLAM. I propose a method that represents the virtual transmitter by a Gaussian mixture model and learns its parameters. The evaluation shows that the proposed method outperforms the previous approach while decreasing the computational load. Also, the current methods for radio channel estimation yield a considerable computational load that prohibits a real-time deployment. The thesis investigates the possibility of using artificial neural networks trained to estimate the number of multipath components and corresponding delays in a noisy measurement of a channel impulse response. The artificial neural network-based delay estimator provides a superresolution performance and faster runtime than the classical approaches. The precision of the trained artificial neural network architecture is evaluated and compared to the Cramer-Rao lower bound theoretical limit and classical channel estimation algorithms

Simultaneous Localization of a Receiver and Mapping of Multipath Generating Geometry in Indoor Environments

This paper presents an algorithm that aims at combining the pre-existing and dominating applications of radio signals, namely communication, navigation and sensing of an environment into an integrated approach. In wireless propagation the transmitted signal is reflected and scattered by objects. Especially in indoor or urban scenarios, the signal reaching the receive antenna consists of multiple paths, called multipath. Multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. With Channel-SLAM we introduced an algorithm which uses multipath components (MPCs) for positioning instead of mitigating them. In this paper, we show that MPCs allow us in addition to estimating the position of a receiver, to estimate the locations of reflecting surfaces and scatterers. We show that these estimations relate to floor plans of indoor environments. To verify the proposed algorithm, we evaluate the algorithm based on measurements using an ultra-wideband (UWB) system, the Decawaves DW1000 UWB transceiver

Stochastic Data Association for Multipath Assisted Positioning Using a Single Transmitter

This paper builds on and extends the Channel-SLAM algorithm which exploits a multipath radio channel for the position estimation of mobile receivers. Channel-SLAM treats Multipath Components (MPCs) as Line-of-sight (LoS) signals originating from Virtual Transmitters (VTs) and estimates the positions of VTs and receiver simultaneously based on Bayesian filtering. The current Channel-SLAM implementation does not involve the retracking of previous MPCs or VTs. Therefore, when the tracking of an MPC is lost and, subsequently, regained, the corresponding VT is initialized without any prior information. Incorporating a stochastic data association algorithm extends Channel-SLAM and enables the retracking of VTs even when the MPC has been lost. The proposed algorithm increases positioning reliability, decreases computation complexity, and improves the precision of Channel-SLAM. Additionally, this paper presents a novel transition model using inertial sensors for a hand-held device for moving pedestrians. The developed positioning algorithm is evaluated based on measurement data obtained in an indoor scenario using an off-the-shelf Ultra-WideBand (UWB) module. Evaluations show that accurate position estimation can be done using only one physical transmitter and without requiring any knowledge of the physical transmitter position

Radio Interference Measurements for Urban Cooperative Intelligent Transportation Systems

The trend towards urbanization increases the need for highly available public transportation. Nevertheless, the majority of society demands for individual transport too. To address these demands in urban areas, intelligent transportation systems (ITS) aim at increasing capacity and safety while reducing costs, accidents, and environmental impact. To that end, both the rail-way industry and the automotive industry focus on automation, digitization, and wireless communications to cope with increasing numbers of vehicles and passengers. These two industries may rely on cooperative ITS (C-ITS) communicating in the same frequency band. Without appropriate measures, interference between the different radio technologies must be assumed and reliable communication for safety-critical applications cannot be guaranteed. To develop accurate and realistic interference models for current and future radio technologies, we conducted a four-day measurement campaign with the Deutsche Bahn (DB)advanced TrainLab on the Berlin “Süd-Ring” tracks. In this paper, we present an overview on C-ITS radio technologies, the measurement campaign, first results, and conclusions. An initial data analysis shows that adjacent channel interference can cause severe performance degradation on urban rail C-ITS if generated in line-of-sight (LOS) to the train with a significant number of interfering signals