## 2 State-of-the-Art Overview and Problem Formulation
### Overview of Positioning Techniques  
In general, positioning techniques are categorized in several different ways [25, 26], wherein each category may differ in terms of various physical media signal used (e.g., sound, light, or radio signal), or different principles of obtaining the location of the target of interest (e.g., proximity, fingerprinting, or multilateration). Alternatively, the positioning techniques stem from a specific positioning system, which can be classified into two groups: self-positioning systems and remote-positioning systems ([27] Ch. 1). While the former is essentially a DR system [28], as it computes the current location based on the previous location by fusing the heading and the distance measurements obtained from the inertial measurement unit (IMU) sensors, the remote-positioning system is referred to as a radio-positioning system, as it relies on the LRMs wherein the relative location information is embedded. Typical LRMs consists of RSSI, time of arrival (ToA), and/or angle of arrival (AoA).  
For a radio-positioning system, the acquisition of the LRMs relies on different equipment and/or methods. Owing to the simplicity of acquisition and low-complexity in terms of the needed hardware, RSSI-based positioning remains an attractive approach especially for indoor scenarios ([27] Ch. 11). However, several error sources, such as multi-path and NLoS, impair the stability and prediction quality of the RSSI measurements and therefore may keep the positioning accuracy away from being acceptable by the requirements specified for certain use cases [29]. In addition, the ToA measurements can also be employed for positioning by using a multilateration principle ([27] Ch. 6), such as the global navigation satellite system (GNSS)-based positioning. Conditional on a perfect synchronized clock among the UE and AP, the ToA-based positioning is capable of achieving much higher accuracy than the RSSI-based positioning especially at higher carrier frequencies and over wider bandwidths [30]. However, due to inherent clock offset among transmitters and receivers, certain approaches are required to compensate for the synchronization errors [31; 32]. Last but not least, there is also the AoA-based positioning enabled by antenna arrays or directional antennas ([27] Ch. 9), such that the direction from which the signal arrives at the receiver (i.e., the AP in a network-centric positioning system) is known. Hence, a location estimation is obtained by applying the multi-angulation principle ([33] Ch. 2). Under both LoS and NLoS propagation, the antenna orientation at the receiver side must be known or estimated to make the AoA measurements reasonable for positioning.  
Besides the positioning principles for different LRMs, various algorithms can be applied to learn the location estimates based on one or multiple LRMs ([27] Ch. 2, [34; 35]). In this work, the robot location is acquired by combining the radio-positioning techniques (for the estimation of initial location of the robot) and the self-positioning (for subsequent positioning) by utilizing the outputs of the on-board sensors to predict the moving distance and the direction of the robot at each time instant.