## 1 Introduction  
Coordination of multiple drones has been intensively studied in the field of military surveillance, smart agriculture, logistics, disaster response, and artistic drone shows [1, 2]. The use of swarm of small aerial vehicles allows for extended mission area, flexible mission capability, robustness to single point failure, and cost effectiveness. Research areas related to swarm drones have been very diverse, including development of small-scale aerial vehicles [3, 4, 5], ad hoc communication backbone tailored to swarm operation [6, 7, 8], path generation to ensure collision avoidance [9, 10, 11], mission-level planning and scheduling to achieve high-level autonomy [12, 13, 14, 15], and interaction/interface between the human operator and the swarm drones [16, 17, 18, 19]. It should be noted that while earlier literature focused on technologies to enhance performance and capabilities, recent work has been looking into more safe, secure, and reliable operations of such swarm systems [20, 21, 22, 23]. In order to operate in a more advanced, robust, scalable and flexible manner, research with self-regulation and environmental adaptation is expanding into the design of new coordination algorithms that combine biological processes [24]. For the safe and reliable operation of swarm drones, it is crucial to be able to monitor/manage the health of the vehicles and to take prognostic actions when needed. The system health may be affected by many aspects such as faults in the actuator and sensors in the drone system, defects in the communication link, and possibly cyberattacks by malicious entities; identification of the culprit requires incorporation of detailed knowledge of the system, mission, and the environment. That said, the first step toward health management is to detect an anomaly in the system behavior--in other words, to recognize something goes wrong (even without clear identification of what went wrong). Unfortunately, the current practice of health monitoring for swarm drones often relies solely on the human operator's experience and expertise--eyeballing flying drones' motion and/or looking at the trajectory data on the ground station screen. Deeper post-flight investigation can certainly be performed, but on-site monitoring of vehicle health is critical to enable execution of an appropriate contingency plan and to avoid secondary damage due to a chain reaction of failures. As such, it is imperative to devise an automated procedure to monitor and detect an anomaly of flying swarm drones to support the operator's correct situational awareness and responsive decision making. Traditional model-based methods have been intensively studied in the context of fault detection and isolation (FDI) particularly for safety-critical aerospace systems [25; 26]. This has recently been extended to multi-unmanned aerial vehicle (UAV) systems from the perspective of safe operations [27; 28; 29] and also for resilience against cyberattacks [30; 31; 32; 33]. In a multi-UAV system, the design and experimental verification methods and strategies have been studied to perform fault detection, identification, and recovery (FDIR) by constructing a cooperative virtual sensor (CVS) system for each UAV through on-board sensor signals [34]. These model-based schemes work well when normal behavior of the system is well-understood and predicted using (physics-based) models and also when the potential fault modes are well-identified a priori so that a finite number of hypotheses on the faultiness can be posed. However, this is not often the case for a flight of swarm drones. From the mission perspective, one reason to utilize the swarm is their robustness to single-point failure; however, it is still critical to ensure the safety and health of each vehicle that may affect the overall performance. In particular, a least thus far, small-sized drones are not designed under a high-reliability requirement; thus, the main parameters associated with motion models of the drones are prone to uncertainties.