Natássya Barlate Floro da Silva

Performance evaluation of the Extended Kalman Filter and Unscented Kalman Filter

The Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are methods usually applied in the sensor fusion for Unmanned Aerial Vehicles due to its nonlinear navigation equations. This paper presents a comparison between the two filters considering the position, velocity and attitude of the vehicle and the IMU bias. The simulation experiments are designed according to performance evaluation techniques for two trajectories and different state vectors. The results show that the EKF has a lower computational cost than UKF, but the latter provides smaller errors for most of the states. It also show that the bias estimation influences positively the solution granted by the EKF.

Case studies of performance evaluation of cryptographic algorithms for an embedded system and a general purpose computer

Embedded systems are associations between hardware and software designed to perform a specific function. These systems are usually part of a larger system and their wireless communications are a hallmark. Therefore, it is important to guarantee a secure communication by ensuring the confidentiality of information, which is obtained through cryptography. Security has not traditionally been considered a requirement in embedded systems design and the application of specific security techniques to these devices is still incipient. This paper presents a performance evaluation analysis of cryptographic algorithms in embedded systems (namely RC2, AES, Blowfish, DES, 3DES, ECC and RSA). Parameters considered in the analysis are average processor and memory usage, response time and power consumption. The results show that symmetric and asymmetric algorithms such as Blowfish and ECC have a good performance in embedded systems when properly chosen for each situation.

The HAMSTER Data Communication Architecture for Unmanned Aerial, Ground and Aquatic Systems

This paper presents HAMSTER, the HeAlthy, Mobility and Security based data communication archiTEctuRe. HAMSTER is designed for Unmanned Vehicles and addresses mainly three types of communications: machine-to-machine, machine-to-infrastructure and internal machine communications. It is divided into three main versions: Flying HAMSTER (for aerial systems), Running HAMSTER (for terrestrial systems) and Swimming HAMSTER (for aquatic systems). Every version of such architecture is also equipped with Sphere and Nimble. Sphere deals with Safety & Security aspects regarding communication, components “health” and modules authentication. Nimble is aimed at increasing the overall mobility in such scenarios, strongly actuating with inherent communications of each application field. This paper details every aspect of HAMSTER and presents, as a plus at the end, two case studies: the first one consists of an evaluation of five communications schemes for internal communications in airplanes; the second one is a cryptographic evaluation of two Elliptic Curve Cryptography algorithms.

A new concept of VTOL as fixed-wing

UAVs (Unmanned Aerial Vehicles) have been used in several applications, which include remote sensing, precision agriculture and atmospheric data monitoring. These aircraft developments have had major improvements taking advantage of the miniaturization of electronic components and modules such as GPS receivers, digital cameras, wireless communication equipment and sensors. Vertical Takeoff and Landing (VTOL) are aircrafts that can take off and land vertically, like helicopters. However, the rotary-wing machines tend to suffer performance penalties. This paper proposes a different configuration of VTOL with fixed-wings and autonomous procedures. This VTOL has the advantages of traditional aircrafts and can takeoff and land in small areas. Its design, flight modes and autopilot structure are described and a specific scenario of application is defined.

Data Fusion Techniques Applied to Takeoff and Landing Procedures ‑ A VTOL Case Study

One of the challenges in the development of Unmanned Aerial Vehicles is the use of a sensor suite, which provides crucial information estimation for the navigation system, in a way that assures data quality and reduced costs. AVALON is a fixed wing VTOL (Vertical TakeOff and Landing) aircraft whose automatic takeoff and landing stages require an adequate estimation of the altitude. To improve the estimation of the current sensor suite configuration, this paper analyses the addition of sonar information during the takeoff and landing stages of AVALON.

3D Path-Following Algorithms for Unmanned Aerial Vehicles Adjusted with Genetic Algorithm

Unmanned Aerial Vehicle (UAV) is a growing research topic due to its wide range of applications. One of its major challenges is the development of the autopilot, responsible for keeping the aircraft in desired flight conditions and for executing navigation tasks. A navigation task that is usually necessary is the path-following, which guarantees that the aircraft follows a predefined trajectory. It is possible to find several approaches for this function, based in geometric and control techniques; however, compared only for the 2D scenario. Therefore, this paper objective is to present new extended path-following algorithms for the 3D scenario, based in the well-known path-following algorithms Lookahead, Non-Linear Guidance Law (NLGL), Pure Pursuit and Line-of-Sight (PLOS) and Vector Field. The algorithms parameters are obtained with Genetic Algorithm optimisation and a comparison between all of them is performed in an environment with and without wind. The results from the simulations show that Vector Field has the best performance and PLOS has the worse one due to a high effort demanded.

Comparison of 3D path-following algorithms for unmanned aerial vehicles

Unmanned Aerial Vehicle (UAV) development is a field with rising interest in recent years. One of its essential part is the autopilot, responsible for keeping the aircraft in desired flight conditions and for executing navigation tasks. Among the navigation tasks, one that stands out is the path- following operation, which guarantees that the aircraft follows a predefined trajectory. The literature presents a variety of approaches with this function, based in geometric and control techniques; however, compared only for the 2D scenario. Therefore, the main purpose of this paper is to perform a comparison in the 3D scenario of the path-following algorithms Lookahead, Non-Linear Guidance Law (NLGL), Pure Pursuit and Line-of-Sight (PLOS) and Vector Field, regarding accuracy and computational cost. The simulation results of straight lines trajectories show that PLOS and Lookahead present lower errors and lower computational cost than NLGL and Vector Field, thus being more adequate.

Attitude estimation of a simulated flight and GPS positioning with Kalman filter

The Kalman filter is an algorithm used for data estimation and sensor fusion. It has been used for decades in several projects, especially in the aerospace industry, being the Apollo program for lunar explorations one of the first to apply the filter. As a way to analyze the practical Kalman filter application in real situations, this paper shows its use in the estimation of the horizontal attitude for an aircraft flight. The experiments were produced inside the X-Plane simulator, through its plugin system and with the estimation of data gathered from the simulated gyroscope. Additionally, a mobile application was developed to show the Kalman filter output for GPS positioning estimation under uncertainty conditions - in this case, inside a building. Results show the data obtained by the filter and a comparison of the values before and after its application.