# A Real-Time Simulator for Navigation in GNSS-Denied Environments of UAV Swarms
###### Abstract  
Accurate localization is the foundation of unmanned aerial vehicle (UAV) swarm applications in the global navigation satellite system (GNSS)-denied environment. However, the implementation of UAV formation in the real world is costly and time-consuming, which leads to difficulties in developing navigation algorithms. A real-time simulator for navigation in GNSS-denied environments is proposed, which includes world, model, controller, scene matching navigation (SMN), relative navigation and formation controller modules. Each module can be modified, which means that users can test their own algorithms. A novel inertial-aided SMN (ISMN) algorithm is developed and a relative navigation method that does not rely on inter communication is proposed. ISMN and relative navigation based on a camera and ultrawideband (UWB) are tested on the platform. Based on the developed simulation system, the navigation algorithms can be verified easily, which can reduce the time and personnel requirements during flight testing. UAV swarms; simulation platform; ISMN; relative navigation Article  
A Real-Time Simulator for Navigation Environments of UAV Swarms  
[PERSON], Chen, and  
becoming increasingly fierce. Currently, countermeasures such as interference, deception and attacks against GNSS signals and systems are emerging one after another, and the operating environment of satellite navigation systems is gradually deteriorating. The potential threats to the GNSS system mainly include the following: (1) directly killing or interfering with navigation satellites through anti-satellite weapons; (2) the use of radio interference, information security attacks and other means to target ground operation and control stations and other facilities, which makes it difficult for these ground operation and control facilities to function properly, resulting in the inability of the entire navigation system to function properly; (3) using various methods such as suppression interference and deception to directly interfere with the navigation terminal, making it unable to output or mistakenly output navigation information, thereby interfering with the normal use of the navigation system. On the other hand, GNSS signals are affected when passing through environmental objects such as trees, buildings and rocks. These obstacles can cause signal reflection, scattering, refraction, etc., resulting in longer signal propagation paths, weakened signal strengths, phase delays, etc. These impacts can lead to a decrease in the reception quality of GNSS signals, thereby affecting the accuracy of position calculation [4]. Therefore, navigation methods that do not rely on communication under GNSS-denied environments have become increasingly important for UAV swarm applications. Due to the complexity of outdoor environments, UAV formation flying requires a large number of personnel, which leads to high development costs and large time requirements for navigation algorithms. Currently, open-source simulation systems such as Prometheus [5], Xtdrone [6; 7] and Rflysim [8; 9] are more concerned with single control or swarm control, and, in the early stages of developing relative navigation algorithms, it is necessary to verify them in simulation systems to achieve the rapid iteration and validation of the algorithms. Visual navigation calculates the pose of UAVs relative to ground features to achieve positioning and navigation. Visual navigation has the advantage of minimal environmental impact and high accuracy and has been widely used in GNSS-denied environments [10]. Benefiting from the low cost and high-precision positioning, the SMN system possesses key potential in the GNSS-denied environment [11]. In [12], a novel SMN is proposed, and an optimized factor of the homography matrix is used to reduce the projection errors. In the experiment, the UAV flies at a 150 m height and the flight speed is 12 m/s.