Sensor Based System Identification in Real Time for Noise Covariance Deficient Models

System identification methods have extensive application in the aerospace industry’s experimental stability and control studies. Accurate aerodynamic modeling and system identification are necessary because they enable performance evaluation, flight simulation, control system design, fault detection, and model aircraft’s complex non-linear behavior. Various estimation methods yield different levels of accuracies with varying complexity and computational time requirements. The primary motivation of such studies is the accurate quantification of process noise. This research evaluates the performance of two recursive parameter estimation methods, viz.; First is the Fourier Transform Regression (FTR). The second approach describes the Extended version of Recursive Least Square (EFRLS), where E.F. refers to the Extended Forgetting factor. Also, the computational viability of these methods was analyzed for real-time application in aerodynamic parameter estimation for both linear and non-linear systems. While the first method utilizes the frequency domain to evaluate aerodynamic parameters, the second method works when noise covariances are unknown. The performance of both methods was assessed by benchmarking against parameter estimates from established methods like Extended Kalman Filter (EKF), Unscented Kalman Filter (UNKF), and Output Error Method (OEM)

Autonomisen multikopteriparven hallinta etsintä- ja pelastustehtävissä

This thesis presents the requirements and implementation of a Ground Control Station (GCS) application for controlling a fleet of multicopters to perform a Search And Rescue (SAR) mission. The requirements are put together by analysing existing drone types, SAR practices, and available GCS applications. Multicopters are found to be the most feasible drone to use for the SAR use case because of their maneuverability, despite not having the best endurance. Several existing area coverage methods are presented and their usefulness is analyzed for SAR scenarios where different amounts of prior knowledge is available. It is stated that most search patterns can be used with a fleet of drones, by creating drone formations and by dividing the target area into sub-areas. It is noted that most currently available GCS applications are focused on controlling a single drone for either industrial or hobby use. A proof of concept prototype is developed on top of an open source GCS and tested in field tests. Based on all the previous learnings from the protype and research, a new GCS is designed and developed. The development on optimizing communications between the GCS and the autopilot leads to a filed patent application. The new software is tested with three multicopters in a water rescue scenario and several user interface improvements are made as a result of the learnings. The development of a GCS for controlling a drone fleet for search and rescue is proven feasible.Työssä esitetään multikopteriparven hallintaan käytettävän Ground Control Station (GCS) ohjelmiston vaatimukset ja toteutus Search And Rescue (SAR) etsintä- ja pelastustehtävien suorittamiseksi. Vaatimukset kootaan yhteen analysoimalla saatavilla olevia droonityyppejä, SAR pelastuskäytäntöjä, sekä GCS ohjelmistoja. Multikopterit osoittautuvat liikkuvuutensa ansiosta pelastustehtäviin sopivimmaksi vaihtoehdoksi, vaikka niiden saavutettavissa oleva lentoaika ei ole parhaimmasta päästä. Erilaisia etsintämetodeja esitetään alueiden kattamiseksi ja niiden hyödyllisyyttä analysoidaan SAR tilanteissa, joissa ennakkotietoa on saatavilla vaihtelevasti. Osoitetaan, että useimpia etsintäalgoritmeja voidaan hyödyntää drooniparvella, muodostamalla lentomuodostelmia, sekä jakamalla kohdealue pienempiin osa-alueisiin. Huomataan, että suurin osa tällä hetkellä saatavilla olevista GCS ohjelmistoista on suunnattu teollisuuden tai harrastelijoiden käyttöön, pääasiassa yksittäisen droonin hallintaan. Prototyyppi kehitetään avoimen lähdekoodin GCS ohjelmiston pohjalta ja testataan kenttätesteissä. Tästä saadun tiedon avulla suunnitellaan ja kehitetään uusi GCS ohjelmisto. Kehitystyö viestinnän optimoinniksi autopilotin ja GCS ohjelmiston välillä johtaa patenttihakemukseen. Uusi ohjelmisto testataan kolmella multikopterilla vesipelastustilanteessa ja sen seurauksena käyttöliittymään tehdään useita parannuksia. GCS ohjelmiston luominen drooniparven hallintaan etsintä- ja pelastustehtävissä todetaan mahdolliseksi

A Hierarchical Architectural Framework for Securing Unmanned Aerial Systems

Unmanned Aerial Systems (UAS) are becoming more widely used in the new era of evolving technology; increasing performance while decreasing size, weight, and cost. A UAS equipped with a Flight Control System (FCS) that can be used to fly semi- or fully-autonomous is a prime example of a Cyber Physical and Safety Critical system. Current Cyber-Physical defenses against malicious attacks are structured around security standards for best practices involving the development of protocols and the digital software implementation. Thus far, few attempts have been made to embed security into the architecture of the system considering security as a holistic problem. Therefore, a Hierarchical, Embedded, Cyber Attack Detection (HECAD) framework is developed to provide security in a holistic manor, providing resiliency against cyber-attacks as well as introducing strategies for mitigating and dealing with component failures. Traversing the hardware/software barrier, HECAD provides detection of malicious faults at the hardware and software level; verified through the development of an FPGA implementation and tested using a UAS FCS