Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Mechanical and Aerospace Engineering

Committee Chair

Mario Perhinschi

Committee Co-Chair

Patrick Browning

Committee Member

Patrick Browning

Committee Member

Christopher Griffin

Committee Member

Jason Gross

Committee Member

Majid Jaridi


In this research, the artificial immune system (AIS) paradigm is used for the development of a conceptual framework for autonomous flight when vehicle position and velocity are not available from direct sources such as the global navigation satellite systems or external landmarks and systems. The AIS is expected to provide corrections of velocity and position estimations that are only based on the outputs of onboard inertial measurement units (IMU). The AIS comprises sets of artificial memory cells that simulate the function of memory T- and B-cells in the biological immune system of vertebrates. The innate immune system uses information about invading antigens and needed antibodies. This information is encoded and sorted by T- and B-cells. The immune system has an adaptive component that can accelerate and intensify the immune response upon subsequent infection with the same antigen. The artificial memory cells attempt to mimic these characteristics for estimation error compensation and are constructed under normal conditions when all sensor systems function accurately, including those providing vehicle position and velocity information. The artificial memory cells consist of two main components: a collection of instantaneous measurements of relevant vehicle features representing the antigen and a set of instantaneous estimation errors or correction features, representing the antibodies. The antigen characterizes the dynamics of the system and is assumed to be correlated with the required corrections of position and velocity estimation or antibodies. When the navigation source is unavailable, the currently measured vehicle features from the onboard sensors are matched against the AIS antigens and the corresponding corrections are extracted and used to adjust the position and velocity estimation algorithm and provide the corrected estimation as actual measurement feedback to the vehicle’s control system. The proposed framework is implemented and tested through simulation in two versions: with corrections applied to the output or the input of the estimation scheme. For both approaches, the vehicle feature or antigen sets include increments of body axes components of acceleration and angular rate. The correction feature or antibody sets include vehicle position and velocity and vehicle acceleration adjustments, respectively. The impact on the performance of the proposed methodology produced by essential elements such as path generation method, matching algorithm, feature set, and the IMU grade was investigated. The findings demonstrated that in all cases, the proposed methodology could significantly reduce the accumulation of dead reckoning errors and can become a viable solution in situations where direct accurate measurements and other sources of information are not available. The functionality of the proposed methodology and its promising outcomes were successfully illustrated using the West Virginia University unmanned aerial system simulation environment.