# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 1 Introduction  
Numerous applications, including construction, surveying, agriculture, and autonomous vehicles, which demand centimeter-level precision, necessitate precise relative positioning. Real-time kinematic (RTK) systems based on the carrier-based differential global navigation satellite system (GNSS) have satisfied these extremely stringent accuracy requirements [1, 2, 3, 4]. An RTK system typically uses double difference (DD) code and carrier phase measurements of the base and rover receivers to achieve centimeter-level relative positioning accuracy [5]. To use carrier phase measurements as precise ranging sources, integer ambiguities in the carrier phase measurements must be resolved and the high level of positioning accuracy is possible only when the integer ambiguities associated with the double difference measurements are correctly resolved. Among various methods for integer ambiguity resolution and validation [6, 7], Least-squares Ambiguity Decorrelation Adjustment (LAMBDA) algorithms [8, 9, 10] with ratio test have gained the most popularity. While a typical RTK system comprises a static reference receiver and a rover, there are instances where a moving reference station is necessary for RTK applications. [PERSON] et al.,developed a navigation architecture for ship-relative positioning during aircraft carrier landings, utilizing a moving baseline RTK [11; 12; 13]. Related to that, [PERSON] devised fault detection methods for reference receivers and formulated protection level equations specific to ship-relative positioning [14; 15; 16]. [PERSON] et al. introduced methods enabling precise operations of ground vehicles and drones solely through moving baseline RTK, extending the benefits of RTK through correction data transmission from ground vehicles [17]. [PERSON] et al. proposed positioning algorithms for single-frequency multi-constellation GNSS systems based on moving baseline RTK [18]. Additionally, [PERSON] et al. applied moving baseline RTK principles to address the health monitoring of bridges, proposing suitable measurement models for the task [19]. Regardless of whether the base station remains stationary [20] or is in motion, both LAMBDA and ratio tests are equally capable of resolving and validating integer ambiguities. Furthermore, while resolving integer ambiguities allows for the computation of the relative position vector between the base station and a rover in a similar manner, it is essential to note that achieving the same level of RTK performance as with a static base station may prove challenging for small unmanned aerial vehicles (UAVs) having low-cost GNSS receivers and antenna. This difficulty arises due to dynamic baseline changes experienced by UAVs, compounded by the large noise levels in code and carrier phase measurements. Also, considerable differences in measurement times between GNSS receivers may exist. Furthermore, frequent satellite inclusion and exclusion may occur during maneuvers, potentially resulting in poor performance of an RTK process. In summary, compared to the relatively straightforward scenario involving a stationary base station, achieving RTK performance between moving UAVs with a dynamic baseline poses greater challenges. Addressing these challenges comprehensively is crucial for ensuring seamless RTK performance and should consider the below problems that have been rarely discussed in the literature, as far as the author is aware. * GNSS Measurements Time Synchronization between UAVs;
* Cycle slip detection strategy for a UAV;
* Adopting Dual Kalman filter (KF) structure to cope with ratio test failures owing to relatively inaccurate estimates of newly added states. First, if there is a relatively large difference in measurement time when processing measurements from low-cost receivers on UAVs, the difference should be adjusted as if they were measured simultaneously. The ranging measurement offsets resulting from this time difference are not a problem when the location of the base station is precisely known [21].