## 2 Related Work  
A robotic GLS for UAVs is a technologically and economically viable solution to accomplish mixed-initiative UAV missions. To estimate the target position of UAVs, technologies such as BLE and intelligent signal processing are used [13].  
An octagonal platform was introduced by [14] for autonomous landing. The design has sloped sides to guide the UAV into place as it lands, and it can store and charge up to eight batteries, which are rotated by a continuous motion servo. A battery replacing platform was also addressed by [11] with the implementation of a vision positioning system and a repositioning process using robotic arms powered by servomotors. The arms are used to lock the UAV for battery exchange. Another interesting battery changer station is the one made by [12]. The design features a dual-drum structure that functions like a Ferris wheel to store and retrieve the batteries. These are contained inside a carriage that slides into a receiver attached to the UAV. The structure allows a quick battery change in one linear movement as the charged one pushes the used battery to the other drum. Another interesting approach was proposed by [15]. The station consists of a two-tiered rack which has on the base plate a movable rocker arm and a rotating carousel; meanwhile, the top plate is a landing guide. The battery swapping is performed by a robotic arm that moves the battery holders from the platform to the UAV. The landing guide provides a stable and rigid structure for the quadcopter to land precisely. The positioning system uses LiDAR sensors and a PIXY vision sensor with on-board off-the-shelf color codes detection. [PERSON] et al., in [16], designed an induction battery charging station that consists of a platform with a mobile coil that positions itself under the UAV after its landing. The advantage of this platform is that the battery can be recharged regardless of the UAV orientation. Although the previous designs are interesting, these systems incorporate a complex actuated mechanism and expensive sensors or vision components for real-time tracking and UAV landing.  
Regarding the UAV position estimation techniques, RSSI-BLE represents a promising option for air-finder localization systems. BLE operates in the 2.4-GHz frequency ISM band, and the devices offer a low cost and powerful alternative to other solutions [9; 17]. In [18], a fast localization algorithm was developed using a channel propagation model. The algorithm estimates the position with BLE devices achieving average errors of 0.38 and 0.57 m. The most interesting feature of this algorithm is the target determination. The work presented in [19] proposes the use of a second-order Butterworth bandpass filter to obtain a smoother signal with less dispersion of the RSSI signals. The authors consider that BLE is a good candidate for target positioning scenarios. In [20], a wireless network of four BLE nodes is proposed. The algorithm consists of two phases: training and online locating. To determine the distance, an attenuation model for RSSI is used, with a pre-processing Gaussian filter. Another feature of this method is the active learning ability of BLE nodes. Every node adjusts its pre-trained model according to the received RSSI signals, improving the target position accuracy. Their experiments show an error probability of less than 1.5 m. In [21], several antennas for RSSI stabilization are proposed due to high dispersion on RSSI values, fading, and shadowing, among other problems. Furthermore, the research states some interesting RSSI combination techniques such as Equal Gain Combiner, i.e., the mean of the RSSI obtained at the different antennas, or the Maximum Ratio Combiner (MRC). The research concludes that MRC achieves the best results on RSSI detection.  
Although some state-of-art studies present similar results in RSSI-BLE target localization or the design of a UAV ground station, in this work, we propose a complete framework for landing with an accurate MLE-based target position estimation.