# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 2 Related Work
### Precise Outdoor Positioning  
An outdoor positioning system is a system that identifies and tracks the location of objects or people in real time in an outdoor environment. Traditionally, this global location has been provided by a GNSS system. The GNSS data are usually combined with an Inertial Navigation Solution (INS) in order to monitor at a high rate the exact location of the target. As previously mentioned, fusing data from these sources can provide, under good GNSS conditions (clear view of the sky), a reliable and accurate position. However, there are certain environments in which the performance of GNSS systems is compromised due to tunnels and obstacles that block the satellite signals. In these environments, the GNSS receiver situated on the target is not able to track a sufficient number of satellites for computing an accurate position. Or in the worst case, the GNSS receiver completely looses the signals from all the satellites. Hence, one of the main challenges of outdoor positioning is to face the possible interferences and signal outages of GNSS systems. In order to overcome these situations, different positioning technologies have been studied. One of these technologies is UWB which can be used for enhancing the positioning performance and guaranteeing a continuity and availability in the positioning. In the literature, several examples based on the fusion between UWB sensor-based and INS sensor-based positioning systems can be found. This sensor fusion allows to remove or minimize the limitations of each technology, enhancing the accuracy and reliability of the final positioning solution. Traditionally, UWB positioning solutions have been used for tracking in indoor environments, since the UWB radio technology is capable of providing accurate positioning information. In [6] the indoor positioning performance of an Ultra-Wideband/Inertial Measurement Unit (UWB/IMU) solution based on an 8-state EKF is proposed and tested in real time. The dynamic test results, carried out in a simulated platform and also in a real indoor environment, show better performance of this fusion than using an stand-alone UWB positioning solution. In [7], a Sage-Husa Fuzzy Adaptive Filter (SHFAF) solution based on INS/UWB positioning is proposed for autonomous indoor mobile robots. Their solution was validated in real time by carrying out dynamic tests in both a simulated environment (using Gazebo simulator) and a real scenario (using a mobile robot). This approach obtained a position estimation error of less than 20 cm in the 88.6% of the measurements. In [8] an UWB/IMU fusion algorithm based on the EKF and Unscented Kalman Filter (UKF) was studied for indoor navigation. The proposed Indoor Positioning System (IPS) was validated in real time in a real environment by carrying out dynamic tests using a three wheeled omnidirectional robot. The obtained results when applying UKF showed a mean positioning distance error of 7 cm and a maximum positioning distance error of 21 cm. In the last years, the literature has shown some Ultra-Wideband/Inertial Navigation System (UWB/INS) positioning approaches for outdoor applications. In [4], an UWB/IMU integrated navigation solution based on a EKF is presented for outdoor environments where GNSS signal reception is limited or impossible. The proposed solution for navigating and positioning vehicles and people was validated in a real environment by using a real vehicle moving at a speed up to 18 km/h. For characterising the positioning performance of the UWB/IMU approach, a GNSS solution was used as ground truth. The presented results showed a Root Mean Square Error (RMSE) positioning distance error of around 30 cm in the best case scenario. In [5], the performance of a tightly-coupled GPS/UWB/INS cooperative positioning scheme for vehicles based on a Robust Kalman Filter (RKF) supported by Vehicle-to-Everything (V2X) communication is presented. The proposed solution was dynamically tested in a real environment by using two people walking with the equipment.