Flight simulators are widely used by the military, civil and commercial aviation. Visual cues are an essential part of helicopter flight. The required cues for hover are especially large due to closeness to the ground and small movements. In this thesis, density and height parameters of the 3D (Three Dimensional) objects in the scene are analyzed to find their effect on hovering and low altitude flight. An experiment is conducted using a PC-based flight simulator with three LCD monitors and flight control set. Ten professional military pilots participated in the experiment. v Results revealed that object density and object height are effective on the horizontal and vertical hovering performance. There is a peak point after which increasing the density does not improve the performance. In low altitude flight, altitude control is positively affected by smaller object height. However, pilots prefer the scenes composed of the high and mixture objects while hovering and flying at low altitude. Distance estimation is affected by the interaction of the object density and height.

ABSTRACT DETERMINATION OF STOCHASTIC MODEL PARAMETERS OF INERTIAL SENSORS &Uuml / nver, Alper PhD, Department of Electric Electronic Engineering Supervisor: Prof. Dr. M&uuml / beccel Demirekler January 2013, 82 pages Gyro and accelerometer systematic errors due to biases, scale factors, and misalignments can be compensated via an on-board Kalman filtering approach in a Navigation System. On the other hand, sensor random noise sources such as Quantization Noise (QN), Angular Random Walk (ARW), Flicker Noise (FN), and Rate Random Walk (RRW) are not easily estimated by an on-board filter, due to their random characteristics. In this thesis a new method based on the variance of difference sequences is proposed to compute the powers of the above mentioned noise sources. The method is capable of online or offline estimation of stochastic model parameters of the inertial sensors. Our aim in this study is the estimation of ARW, FN and RRW parameters besides the quantization and the Gauss-Markov noise parameters of the inertial sensors. The proposed method is tested both on the simulated and the real sensor data and the results are compared with the Allan variance method. Comparison shows very satisfactory results for the performance of the method. Computational load of the new method is less than the computational load of the Allan variance on the order of tens. One of the usages of this method is the individual noise characterization. A noise, whose power spectral density has a constant slope, can be identified accurately by the proposed method. In addition to this, the parameters of the GM noise can also be determined. Another idea developed here is to approximate the overall error source as a combination of ARW and some number of GM sources only. The reasons of selecting such a structure is the feasibility of using these models in a Kalman filter framework for error propagation as well as their generality of modeling other noise sources.