## 1 Introduction  
Accurately determining the position of a mobile robot is crucial to its effective operation. As such, robot localization has been the subject of extensive research since the inception of robotics. Active beacon navigation is a reliable positioning solution that is often implemented in navigation systems for ships, airplanes, and commercial mobile robots. Active beacons with known fixed positions serve as absolute reference points, ensuring highly accurate positioning information is obtained. As a result, this approach offers high positioning reliability. Active beacon technologies are generally based on Time of Flight (ToF) and Angle of Arrival (AoA). ToF consists of precisely measuring the time that a signal travels between an emitter and a receiver. Using the speed of the signal through the medium, the total distance travelled by the signal can be calculated. This distance, combined with the known position of the emitter, can be used to determine the position of the receiver using trilateration or multilateration techniques. This method is used in [1, 2], employing regular ToF and round-trip ToF to calculate the position of the client, obtaining an accuracy of several centimeters. In [3], a ToA method is proposed that combines two different types of beacons that achieve a reduction in the overall frequency that the client needs to sense, allowing the use of much simpler sensors. AoA, on the other hand, uses the angle of the incoming signal relative to the receiver's orientation to estimate the direction of the emitter. By measuring the angle of arrival from multiple emitters with known positions, the receiver's position can be estimated using triangulation techniques.This method is used in [4] to obtain the position of each of the individuals of a swarm of unmanned aerial vehicles. In [5], an alternative method is proposed that utilizes the Angle of Arrival (AoA), which involves an initial estimation of the client's position to adjust the beam forming process, thereby achieving more accurate results. Additionally, other solutions propose merging previous techniques to improve localization precision. In [6], a hybrid method is presented that combines the ToF and AoA, designed to be used in environments with a dense amount of obstacles that hinder the performance of simpler methods. In [7; 8], a method is suggested that combines the ToF and AoA with an angle of departure (AoD)-based method. Although geometry methods, like trilateration or multilateration techniques, are still more widely employed [9], deep learning algorithms are gaining increasing popularity for this purpose. In [10], a method is proposed that employs ToF with a fully connected deep neural network that estimates the position. On the other hand, an AoA-based technique using a shallow neural network is presented in [11]. Active beacons offer the advantage that the data required to estimate the robot's position do not need as much processing compared to other techniques, so low-power solutions are easier to implement in beacon-based systems. In addition, the beacons are fixed and the positions of the beacons relative to the installation are known. This enables every calculated position to be directly related to the installation itself, providing absolute positioning without the necessity for inference. As the prevalence of the Internet of Things (IoT) and robotics continues to rise, the demand for accurate and reliable positioning information at a low cost becomes more important across various applications, including smart cities, industrial settings, public spaces, healthcare, and domestic applications. This has sparked an increasing interest within the research community in the development of lightweight, low-power positioning solutions. In [12; 13], different approaches to the localization of nodes in wireless sensor networks are presented using triangulation to estimate the position of a node. Another current development in the field of indoor positioning is the development of Ultra-Wide-Band Technology (UWB).