Optimal pilot decisions and flight trajectories in air combat

The thesis concerns the analysis and synthesis of pilot decision-making and the design of optimal flight trajectories. In the synthesis framework, the methodology of influence diagrams is applied for modeling and simulating the maneuvering decision process of the pilot in one-on-one air combat. The influence diagram representations describing the maneuvering decision in a one sided optimization setting and in a game setting are constructed. The synthesis of team decision-making in a multiplayer air combat is tackled by formulating a decision theoretical information prioritization approach based on a value function and interval analysis. It gives the team optimal sequence of tactical data that is transmitted between cooperating air units for improving the situation awareness of the friendly pilots in the best possible way. In the optimal trajectory planning framework, an approach towards the interactive automated solution of deterministic aircraft trajectory optimization problems is presented. It offers design principles for a trajectory optimization software that can be operated automatically by a nonexpert user. In addition, the representation of preferences and uncertainties in trajectory optimization is considered by developing a multistage influence diagram that describes a series of the maneuvering decisions in a one-on-one air combat setting. This influence diagram representation as well as the synthesis elaborations provide seminal ways to treat uncertainties in air combat modeling. The work on influence diagrams can also be seen as the extension of the methodology to dynamically evolving decision situations involving possibly multiple actors with conflicting objectives. From the practical point of view, all the synthesis models can be utilized in decision-making systems of air combat simulators. The information prioritization approach can also be implemented in an onboard data link system.reviewe

Dynamic Bayesian Networks as a Probabilistic Metamodel for Combat Simulations

Simulation modeling is used in many situations. Simulation meta-modeling is used to estimate a simulation model result by representing the space of simulation model responses. Metamodeling methods are particularly useful when the simulation model is not particularly suited to real-time or mean real-time use. Most metamodeling methods provide expected value responses while some situations need probabilistic responses. This research establishes the viability of Dynamic Bayesian Networks for simulation metamodeling, those situations needing probabilistic responses. A bootstrapping method is introduced to reduce simulation data requirement for a DBN, and experimental design is shown to benefit a DBN used to represent a multi-dimensional response space. An improved interpolation method is developed and shown beneficial to DBN metamodeling applications. These contributions are employed in a military modeling case study to fully demonstrate the viability of DBN metamodeling for Defense analysis application

Analysis with Dynamic Bayesian Networks Compared to Simulation

This research compares simulations to Dynamic Bayesian Networks in analyzing situations. The research applies models that have known output mean and variance. Queueing systems have theoretical values of the steady-state mean and variance for the number of entities in the system. Monte Carlo simulation development is broken down into two separate approaches: discrete-event simulation and time-oriented simulation. The discrete-event simulation uses pseudo-random numbers to schedule and trigger future events (i.e. customer arrivals and services) and is based on the generated objects.The time-oriented simulation utilizes fixed-width time intervals and updates the system state according to a stochastic process for the set of events occurring during each time period. The accuracy of each approach in estimated by a comparison to the theoretical mean, variance, and probability values

Uncertainty and Error in Combat Modeling, Simulation, and Analysis

Due to the infrequent and competitive nature of combat, several challenges present themselves when developing a predictive simulation. First, there is limited data with which to validate such analysis tools. Secondly, there are many aspects of combat modeling that are highly uncertain and not knowable. This research develops a comprehensive set of techniques for the treatment of uncertainty and error in combat modeling and simulation analysis. First, Evidence Theory is demonstrated as a framework for representing epistemic uncertainty in combat modeling output. Next, a novel method for sensitivity analysis of uncertainty in Evidence Theory is developed. This sensitivity analysis method generates marginal cumulative plausibility functions (CPFs) and cumulative belief functions (CBFs) and prioritizes the contribution of each factor by the Wasserstein distance (also known as the Kantorovich or Earth Movers distance) between the CBF and CPF. Using this method, a rank ordering of the simulation input factors can be produced with respect to uncertainty. Lastly, a procedure for prioritizing the impact of modeling choices on simulation output uncertainty in settings where multiple models are employed is developed. This analysis provides insight into the overall sensitivities of the system with respect to multiple modeling choices

Naval Aviation Squadron Risk Analysis Predictive Bayesian Network Modeling Using Maintenance Climate Assessment Survey Results

Associated risks in flying have resulted in injury or death to aircrew and passengers, and damage or destruction of the aircraft and its surroundings. Although the Naval Aviation\u27s flight mishap rate declined over the past 60 years, the proportion of human error causal factors has stayed relatively constant at about 80%. Efforts to reduce human errors have focused attention on understanding the aircrew and maintenance actions occurring in complex systems. One such tool has been the Naval Aviation squadrons\u27 regular participation in survey questionnaires deigned to measure respondent ratings related to personal judgments or perceptions of organizational climate for meeting the extent to which a particular squadron achieved the High Reliability Organization (HRO) criteria of achieving safe and reliable operations and maintenance practices while working in hazardous environments. Specifically, the Maintenance Climate Assessment Survey (MCAS) is completed by squadron maintainers to enable leadership to assess their unit\u27s aggregated responses against those from other squadrons. Bayesian Network Modeling and Simulation provides a potential methodology to represent the relationships of MCAS results and mishap occurrences that can be used to derive and calculate probabilities of incurring a future mishap. Model development and simulation analysis was conducted to research a causal relationship through quantitative analysis of conditional probabilities based upon observed evidence of previously occurred mishaps. This application would enable Navy and Marine Corps aviation squadron leadership to identify organizational safety risks, apply focused proactive measures to mitigate related hazards characterized by the MCAS results, and reduce organizational susceptibility to future aircraft mishaps

A Study on Techniques/Algorithms used for Detection and Prevention of Security Attacks in Cognitive Radio Networks

In this paper a detailed survey is carried out on the taxonomy of Security Issues, Advances on Security Threats and Countermeasures ,A Cross-Layer Attack, Security Status and Challenges for Cognitive Radio Networks, also a detailed survey on several Algorithms/Techniques used to detect and prevent SSDF(Spectrum Sensing Data Falsification) attack a type of DOS (Denial of Service) attack and several other  Network layer attacks in Cognitive Radio Network or Cognitive Radio Wireless Sensor Node Networks(WSNN’s) to analyze the advantages and disadvantages of those existing algorithms/techniques

Automated Construction of Dynamic Bayesian Networks in Simulation Metamodeling

Tässä työssä esitellään uusi lähestymistapa dynaamisten Bayes-verkkojen (dynamic Bayesian networks, DBNs) automatisoituun konstruointiin simulaatiometamallinnuksessa. DBN-metamallien avulla tutkitaan diskreetillä tapahtumasimuloinnilla (discrete event simulation, DES) luotua simulointidataa. Lähestymistavan avulla kyetään konstruoimaan DBN-metamalleja helposti ja nopeasti tuntematta Bayes-verkkojen toimintaa lähemmin. Aiemmin konstruoitujen DBN-metamallien mahdollisia puutteita voidaan korjata vaivattomasti luomalla uusia paranneltuja metamalleja. Tämä menettely parantaa DBN-metamallien tarkkuutta ja käytettävyyttä. DES on stokastinen simulointimuoto, joka mahdollistaa mallin muuttujien arvojen aikakehityksen tarkastelun. Simulointimetamalleilla tutkitaan simulointimallien ominaisuuksia kuvaamalla niiden sisäänmenojen ja ulostulojen välistä yhteyttä. DBN-metamalleissa DBN kuvaa DES-mallin aikariippuvien muuttujien yhteisjakauman, minkä avulla voidaan tarkastella muuttujien reuna- ja ehdollisten todennäköisyysjakaumien aikakehitystä. Tämä mahdollistaa erilaiset mitä-jos -analyysit, joita ei voida toteuttaa pelkillä sisäänmeno-ulostulokuvauksilla. Tässä työssä esiteltävä lähestymistapa DBN-metamallien automatisoituun konstruointiin koostuu koesuunnittelusta, simulointidatan esikäsittelystä, muuttujakohtaisten ajanhetkien valinnasta DBN:ää varten, DBN:n luomisesta sekä metamallin validoinnista. Automatisoidun mallintamislähestymistavan lisäksi tässä työssä esitellään DBN:ien muuttujakohtaiset aikaskaalat, joiden avulla kyetään konstruoimaan tarkempia DBN:iä kasvattamatta niiden kokoa. Esitettyyn lähestymistapaan perustuen kehitetään DBN-metamallien konstruointityökalu. Työssä havainnollistetaan esitetyn lähestymistavan ja konstruointityökalun käyttökelpoisuutta kahdella esimerkkitapauksella, jotka liittyvät ilmataistelua ja ilmatukikohdan toimintaa kuvaaviin simulointimalleihin.This thesis introduces an automated approach for constructing dynamic Bayesian networks (DBNs) in simulation metamodeling. DBN metamodels permit studies dealing with simulation data produced by discrete event simulation (DES) models. The new approach allows easier and faster construction of such metamodels without requiring detailed knowledge of the methodology of Bayesian networks. Deficiencies in previously created DBN metamodels are thus readily corrected by creating new refined models. This increases the overall accuracy and usability of DBN metamodels. DES is an event based form of stochastic simulation that enables the study of the time evolution of the variables of the underlying system. Simulation metamodels are used to investigate the properties of simulation models by describing their behavior in the form of input-output mappings. In DBN metamodels, a DBN represents the joint probability distribution of the time-dependent variables of a DES model. The utilization of DBNs in metamodeling, unlike the use of input-output mappings, therefore enables investigations involving time-dependent variables. Unconditional and conditional time evolutions, i.e., the evolution over time of marginal or conditional probability distributions, can be studied. This allows for various forms of what-if analysis. The automated approach to the construction of DBN metamodels presented in this thesis includes design of experiment, preprocessing of the simulation data, selection of the variable specific time instants for the DBN, creation of the DBN, and validation of the DBN. In addition, this thesis introduces the concept of multiple time scales in DBNs which allows for more accurate DBNs without increasing their size. An implementation of the approach, a tool for constructing DBN metamodels, is also presented. Constructing DBN metamodels with the tool verifies the practicality of the automated approach. The use of the approach and the tool is illustrated by two example simulation studies dealing with air combat and the operation of an air base

Intelligent Aircraft Maneuvering Decision Based on CNN

© 2019 Association for Computing Machinery. Aiming at the maneuvering decision of aircraft in air combat, an intelligent maneuvering decision model based on convolutional neural network(CNN) is proposed in this paper. Firstly, the situation data, maneuvering decision variables and evaluation indexs are given, and a CNN model that can realize intelligent maneuvering decision is established. Then, according to the evaluation indexes, the structure and parameters of the CNN model are adjusted through the simulation experiments to improve the accuracy and robustness of the maneuvering decision. After that, the validity of the intelligent maneuvering decision model proposed in this paper is verified through comparative experiments that the CNN model can make stable maneuvering decisions with high accuracy. Finally, the flight path in an air combat process is presented