Development of a Full Mission Simulator for Pilot Training of Fighter Aircraft

With aircraft becoming more complex and avionics intensive and flight being almost autonomous based on waypoint navigation, software and displays becoming a significant component of the all glass cockpit of the modern day fighter aircraft, it is imperative that pilots are trained on missions using ground based full mission simulator (FMS) for routine flight as well as advanced missions. A flight simulator is as good as the real system only when it is able to mimic the physical system, both in terms of dynamics and layout so that the pilot gets the complete feel of the environment as encountered during actual sortie. The objective of this research paper is to provide a detailed insight into the various aspects of development of a FMS for pilot training with minimal maintenance operations for long hours of realistic flight training on ground. The approach followed by ADE in developing a FMS using a healthy mix of conventional flight simulation methodologies and novel approaches for various simulator sub-systems to tailor and meet the specific training needs, one presented. The FMS developed by ADE is presently being used by Indian Air Force for flight and mission critical training of squadron pilots

An Interactive Distributed Simulation Framework With Application To Wireless Networks And Intrusion Detection

In this dissertation, we describe the portable, open-source distributed simulation framework (WINDS) targeting simulations of wireless network infrastructures that we have developed. We present the simulation framework which uses modular architecture and apply the framework to studies of mobility pattern effects, routing and intrusion detection mechanisms in simulations of large-scale wireless ad hoc, infrastructure, and totally mobile networks. The distributed simulations within the framework execute seamlessly and transparently to the user on a symmetric multiprocessor cluster computer or a network of computers with no modifications to the code or user objects. A visual graphical interface precisely depicts simulation object states and interactions throughout the simulation execution, giving the user full control over the simulation in real time. The network configuration is detected by the framework, and communication latency is taken into consideration when dynamically adjusting the simulation clock, allowing the simulation to run on a heterogeneous computing system. The simulation framework is easily extensible to multi-cluster systems and computing grids. An entire simulation system can be constructed in a short time, utilizing user-created and supplied simulation components, including mobile nodes, base stations, routing algorithms, traffic patterns and other objects. These objects are automatically compiled and loaded by the simulation system, and are available for dynamic simulation injection at runtime. Using our distributed simulation framework, we have studied modern intrusion detection systems (IDS) and assessed applicability of existing intrusion detection techniques to wireless networks. We have developed a mobile agent-based IDS targeting mobile wireless networks, and introduced load-balancing optimizations aimed at limited-resource systems to improve intrusion detection performance. Packet-based monitoring agents of our IDS employ a CASE-based reasoner engine that performs fast lookups of network packets in the existing SNORT-based intrusion rule-set. Experiments were performed using the intrusion data from MIT Lincoln Laboratories studies, and executed on a cluster computer utilizing our distributed simulation system

Applications for Machine Learning on Readily Available Data from Virtual Reality Training Experiences

The purpose of the research presented in this dissertation is to improve virtual reality (VR) training systems by enhancing their understanding of users. While the field of intelligent tutoring systems (ITS) has seen value in this approach, much research into making use of biometrics to improve user understanding and subsequently training, relies on specialized hardware. Through the presented research, I show that with machine learning (ML), the VR system itself can serve as that specialized hardware for VR training systems. I begin by discussing my explorations into using an ecologically valid, specialized training simulation as a testbed to predict knowledge acquisition by users unfamiliar with the task being trained. Then I look at predicting the cognitive and psychomotor outcomes retained after a one week period. Next I describe our work towards using ML models to predict the transfer of skills from a non-specialized VR assembly training environment to the real-world, based on recorded tracking data. I continue by examining the identifiability of participants in the specialized training task, allowing us to better understand the associated privacy concerns and how the representation of the data can affect identifiability. By using the same tasks separated temporally by a week, we expand our understanding of the diminishing identifiability of user\u27s movements. Finally, I make use of the assembly training environment to explore the feasibility of across-task identifiability, by making use of two different tasks with the same context