## 1 Introduction  
Unmanned aerial vehicles (UAVs) are widely used by military and civilian industries in a huge number of applications such as aerial surveillance and photography and payload deployment [1]. However, with low-cost sensors and electronics, there is a significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industrials. To improve the usage of UAVs in all applications, the ability of the UAV to precisely follow a predetermined path must be considered. For small UAVs, the internal disturbances such as unmodeled dynamics, model uncertainties, and parameter perturbation in addition to external disturbances, such as gust and turbulence, make the design of the flight control system more difficult. Effective path tracking strategies must be accurate and robust to overcome these challenges. The concept of the trajectory tracking problem is to force the vehicle to be in a certain location at a certain time. In a gusty environment or if the external disturbances are not controlled, the vehicle will oscillate around the desired location. Instead of trajectory tracking approach, many researchers adopt path following algorithms to force the vehicle to follow the path rather than going directly to the next waypoint at a specific time. In other words, the difference between the trajectory tracking approach and the path following algorithm is that the latter is time-independent [2; 3]. Diverse path following algorithms have been successively implemented for real flight tests. There are two categories for path following algorithms, one is based on control theories while the other is based on a geometric interpretation [4; 5]. Path following based on the geometric techniques generates guidance command based on the graphical derivation of the desired law. Geometric algorithms are easy for implementation and simple for understanding rather than the control techniques. It includes the pure pursuit guidance law [6], Line of Sight (LOS) guidance law [7], Nonlinear Guidance Law (NLGL) [8], Vector Field-based path following (VF) [1], and carrot chasing path following algorithm [9; 10]. In most cases, as used in this paper, a combination of the pure pursuit guidance law and the LOS guidance law are considered. On the other hand, path following based on control techniques are common in applications because it guarantees system robustness to wind disturbances. A common control path following algorithm is based on proportional-integral-derivative (PID) control [11]. [PERSON] and [PERSON] [12] proposed a modified PID controller with feedforward ability to improve the performance of path following. Gain scheduling approach is also used for trajectory tracking by generating integrated guidance and control algorithms for autonomous vehicles [13]. Linear Quadratic Regulator based path following (LQR) [14] is also a well-known control technique used for UAV path following. The path-following algorithms based on CC uses a virtual target point (VTP) on the path. The guidance laws directly guide the vehicle to chase the virtual point; meanwhile, the vehicle will ride the path. The distance between the VTP and the UAV position projected on the path is often called virtual distance. The selection criteria of the virtual track distance parameter is a trade-off in which the stability of the generated guidance laws is directly affected by its location [15]. The combination of pure pursuit and line of sight-based path following (PLOS) is a geometric technique for generating guidance law. The pure pursuit guidance law guides the UAV to the following waypoint, while the LOS guidance law directs the vehicle to ride the LOS. The weighting sum of both guidance laws forces the UAV to slide on the path to the consequent waypoint [16]. In this research, guidance commands for the UAV orientation are generated using carrot chasing and PLOS with a receding virtual waypoint. The UAV can alter its heading by changing the attitude.