## 7 RF-Based Localisation for Aerial and Ground Robots  
Most of the RF-based localisation is applied to sensor network systems. What makes these attempts non-applicable to mobile vehicles in a straightforward way are the new challenges that the localisation of UAVs and UGVs will provide. * UGVs, and especially UAVs, are highly manoeuvrable, with high speed. The existing state of the art for WSN localisation focuses on fixed targets and cannot address the rapid changes in the target location and the real-time implementation. * The mobility of vehicles calls for a combination of other sensors, such as IMU and Images. The combination of the sensor data, especially images and RF, has not been studied in localisation. * The majority of current works in WSN often consider just 2D cases, while vertical estimation is of great importance in UAV localisation. * The accuracy and robustness in demand in UAVs and UGVs localisation applications are more critical. Usually, very accurate estimation is necessary, while in WSN, rather rough estimation suffices. This, for instance, rules out relying merely on RSS, which is the case for most of the existing state-of-the-art RF localisation. * Use of limited technologies is the other drawback. For robot applications, UWB is used most. It is limited to indoors and is suitable for short range. New Technologies, especially 5G NR, have rarely been considered so far. In 5G, RSS would not be the most relevant feature, so there would be a shift to the use of this technology's new potentials and capabilities. Bayesian filters are among the most used methods for mobile robots localisation, Unscented Kalman Filter [80; 81], Particle Filter [45; 82; 83], Extended Kalman Filter [84; 85; 86; 87]and different variants of it, such as Square Root Cubature Kalman Filter (SRCKF) [88] and Diffusion Extended Kalman Filter (DEKF) [89]. Localisation of a UGV moving with constant speed is developed in [85] using TDOA, incorporating an EKF with an adaptive fading factor to update the prediction covariance to account for the divergence issue of EKF. In a specific scenario [84], localisation is performed for a group of five UAVs, one inside the room and the other four outdoors, based on the information gathered from GNSS, IMU, camera, UWB, and Wi-Fi measurement by all the drones. UWB provides range measurements, and WiFi feeds RSS for indoor UAVs. EKF is used to fuse the measurements and jointly update and estimate all UAVs states and covariance considering the six-DOF (degree-of-freedom) model. An event-driven sampling and transmission mechanism used to counteract the effect of RSS volatility is proposed in [88], where the anchors respond to the mobile robot if some conditions on the received signal are met. The work of [86] combines two EKFs to work simultaneously for location estimation based on TDOA. First, the model is augmented by a weighting filter used in one EKF framework to estimate the state. Then, this estimation is used by the other EKF to update the weights. In a decentralised strategy, [89], diffusion Kalman filter and EKF (DEKF) are integrated to estimate a target based on AOA where a group of UAVs based on AOA share their state estimation, as well as Jacobian information. In [45], PF is incorporated to estimate the position of a robot in 2D space based on RSS. Generating the radio map by collecting training fingerprints, Kernel Density Estimation is included to build the probabilistic observation likelihood for sample importance weights selection. The average of RSS at each point and the variance are recorded in the database. To enhance the accuracy, an adaptive local search is employed to detect and remove unreasonable estimates by limiting the search area. Furthermore, a mechanism is applied to select just the subset of access points with lower variances to reduce the computational burden. The algorithm is tested for four cases with around 1 meter mean accuracy. The author in [90] fuses odometer data and Wi-Fi RSS to track mobile robots using PF.