## 2 Technologies for Indoor Localization
Each beacon has well-known coordinates and operates both as a receiver collecting the signals from the target node and a transmitter by emitting ultrasonic signals. The distance between the beacon and the target node is calculated by measuring the Time-of-Flights (ToF) for the ultrasonic signals, and then the position of the target is evaluated by computing the measured distances. An experimental location error of 10.2 mm in the positioning for a moving robot is reported when the system is operated in the line-of-sight signal conditions. In ref. [22], an ultrasonic localization system using a spring relaxation technique is proposed, which shows an accuracy in the order of 58.6 mm in the 90-percentile. The main drawbacks affecting ultrasound-based localization systems are the difficulty of signals to penetrate well through an obstruction, the need for several numbers of ultrasonic sensors to monitor large areas, multipath sensitivity, temperature sensitivity and the Doppler effect. Magnetic field variations sensing, through dedicated sensor nodes or sensors embedded in portable devices such as smartphones, for positioning estimation have also been addressed. The applicability of a magnetic positioning system as support for a dead-reckoning inertial navigation system for pedestrians has been investigated in [23]. The integrated system has been demonstrated to provide positioning errors of 1 to 2 m over significantly long periods of time up to 45 min in realistic indoor environments. A novel localization and calibration algorithm for an indoor magnetic positioning system employing a one-axis magnetic transmitter and several three-axis field sensors connected to a sensor network is presented in [24]. A calibration scheme for the overall field sensor nonidealities due to magnetic coupling of the sensor coils, coil misalignment, field sensor rotation, and unsynchronized sampling, together with a statistically optimal localization procedure, is presented. The positioning system has been demonstrated in a common office environment with a size of 7 m \(\times\) 5 m \(\times\) 3 m and showed a positioning root-mean-square error (RMSE) of 10.6 cm and an angle RMSE of 6.1\({}^{\circ}\). A positive aspect of magnetic systems, generally highlighted, is that magnetic fields are not blocked by walls or people and can then operate also in non-line-of sight (NLoS) condition. Anyway, as it emerges, magnetic positioning is not suitable for high accuracy. Wireless communication-based indoor positioning systems that employ different radio frequency signals have attracted many researchers. Two main groups of indoor localization systems can be defined: active and passive localization systems [5]. The main difference is the presence or lack of a tag or device used for the sake of localization. In active localization systems, the monitored person carries a dedicated tag or device. Active localization systems exploit RFID [25; 26], UWB [27], Bluetooth [28], ZigBee [29], IR [19], ultrasonic [20], hybrid systems [30] and 802.11 WLANs [31; 32]. Passive localization systems are discussed in [33; 34]. Technologies adopted in passive localization systems include UWB [35; 36] and computer vision [37; 38]. Ready to use (or easy to install) solutions for indoor localization, addressing different application scenarios, are already available on the market. Examples, just to name a few, are the Simatic Real-Time Locating System (RTLS) by Siemens [39], the KIO RTLS by Eliko [40] and the Sewio RTLS [41], designed for industrial applications and based on the UWB technology. The Situm Indoor Positioning System [42] and the HERE Indoor Positioning System [43] are designed for people tracking inside buildings and exploit information from local Wi-Fi and Bluetooth networks, magnetic fields and information from beacons or inertial sensors of smartphones.