# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 1 Introduction
For solving the localization task, most widespread radio wave technologies require an infrastructure of devices installed at known locations, namely anchors. Then, onboard equipment exchanges a signal with the anchors to measure the vehicle-anchor distance, which is then exploited to compute the vehicle location through multilateration algorithms [28]. The vehicle-anchor distance can be measured by leveraging the received signal power [29], or the Time Of Flight (TOF) if the available communication bandwidth is enough [30]. Wi-Fi signals at 2.4 GHz are very widespread, as they can usually take advantage of a pre-existing infrastructure for data exchange inside the plant/warehouse. The Wi-Fi service bandwidth is limited to 85 MHz, which is not sufficient to perform TOF measurements; therefore, distance measurements are done by retrieving electromagnetic path loss models, which aim at linking the received power with the distance from two devices [31; 32]. Unfortunately, received power measurements are strongly affected by the multipath propagation phenomena. Indeed, Wi-Fi fingerprinting techniques can be used to circumvent this problem by avoiding to perform distance measurements with path loss models, at the expense of long calibration procedures, which are hard to make in large industrial environments [33]. Recently, Radio Frequency IDentification (RFID) technology has become popular for mobile robot localization [34], especially passive RFID technology at an ultra-high frequency (UHF) band. Such systems have the advantage of not requiring any power supply to the anchors, but their reading range is limited to few meters; therefore, it could be difficult to implement such a system in a large industrial environment, unless a low-density deployment of RFID anchors is achieved [35]. Ultra-wideband (UWB) systems were expensive in the past; however, nowadays, commercial solutions exist at a reduced cost, by effectively introducing this technology as one of the most promising for performing radio wave-based ranging measurements [36; 37]. The large bandwidth allows for precise and accurate ranging measurements based on the time of arrival (TOA) and time difference of arrival (TDOA) schemes, with a few cm error and with a 20-30 m signal distance coverage for each anchor. Therefore, by deploying a fine-grained infrastructure of UWB anchors, the vehicle location can be estimated through a multilateration algorithm, which leverages the estimated distances from an onboard_UWB tag_[38; 39; 40]. With a pair of UWB tags installed onboard, the vehicle orientation might be measured, too, at the expense of a more complex signal processing [41]. UWB systems suffer from multipath phenomena, and they are not insensitive to the environment [42]. However, thanks to machine learning and other techniques [43; 44; 45; 46; 47], the effect of detrimental multipath or non-line-of-sight (NLOS) conditions can be mitigated. Alternatively, multi-sensor data fusion, also known as sensor fusion, can be used to help the UWB system in the localization task, as proposed in this paper [48; 49; 50; 51; 52; 53; 54; 55; 56]. By generally speaking, sensor fusion localization schemes foresee combining the sensed data from different technologies to obtain a more accurate and reliable location estimation. The fused sensors can be of different natures, and it is common to find in the literature solutions that combine together several _exteroceptive sensors_[49; 50; 51; 52; 54]. However, the installation of multiple _exteroceptive sensor_ systems can be wasteful, in terms of time, cost, and computational burden. Therefore, the fusion of _proprioceptive sensors_ and a single _exteroceptive sensor_ is preferable [55; 56]. Most of these kinds of schemes rely on the usage of recursive dynamic state system estimators, such as the Kalman Filter (KF) and its variants [57], or Monte Carlo estimators, such as the Particle Filter (PF) and its variants [58].