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Many applications require accurate indoor localization. Fingerprint-based localization methods propose a solution to this problem, but rely on a radio map that is effort-intensive to acquire. We automate the radio map acquisition phase using a software-defined radio (SDR) and a wheeled robot. Furthermore, we open-source a radio map acquired with our automated tool for a 3GPP Long-Term Evolution (LTE) wireless link. To the best of our knowledge, this is the first publicly available radio map containing channel state information (CSI). Finally, we describe first localization experiments on this radio map using a convolutional neural network to regress for location coordinates.

Besides being part of the Internet of Things (IoT), drones can play a relevant role in it as enablers. The 3D mobility of UAVs can be exploited to improve node localization in IoT networks for, e.g., search and rescue or goods localization and tracking. One of the widespread IoT communication technologies is Long Range Wide Area Network (LoRaWAN), which allows achieving long communication distances with low power. In this work, we present a drone-aided localization system for LoRa networks in which a UAV is used to improve the estimation of a node's location initially provided by the network. We characterize the relevant parameters of the communication system and use them to develop and test a search algorithm in a realistic simulated scenario. We then move to the full implementation of a real system in which a drone is seamlessly integrated into Swisscom's LoRa network. The drone coordinates with the network with a two-way exchange of information which results in an accurate and fully autonomous localization system. The results obtained in our field tests show a ten-fold improvement in localization precision with respect to the estimation provided by the fixed network. Up to our knowledge, this is the first time a UAV is successfully integrated in a LoRa network to improve its localization accuracy., Comment: Accepted at the IEEE International Conference on Robotics and Automation (ICRA) 2020

Photon detectors like the ALICE Calorimeters are vulnerable to both optical and electrical crosstalk between detector channels belonging to the same particle event. In order to quantify the crosstalk generated by a single channel on its neighbours, the standard method used so far are calibrated electrical pulses in order to quantify the crosstalk between individual frontend preamplifier channels. However due to grounding imperfections, cross-couplings via bias voltage supplies and imperfect electromagnetic shielding, this method introduces biases and is not precise. A new approach is the use of emulated scintillation light over optical fibre bundles in order to effectively generate scintillation light in clustered channels. This method is based on generation of quasi time-coherent light with similar time profiles as scintillation light. Theoretically, and based on linear superposition, pulsing a single channel with calibrated light would be sufficient to measure the crosstalk in the surrounding channels. However, non-linear effects in real detector frontends with energy measurements over cluster sizes like 3x3 can add up to significant errors. Non-linearity is for example due to preamplifier supply voltage drop in the distribution and GND return lines, in particular if power is provided over long cables. This can generate locally reduced or increased signal amplitudes depending on cumulative signal intensity. The presented octal light ALED~(Analogue LED) pulser prototype emulates scintillation light with individually tunable light amplitudes in 8 channels. It can be used for pulsing single channels, or for clustered channels with a predefined amplitude profile. Unbiased single channel crosstalk and non-linear side-effects can be determined for single and multichannel situations.