Guidance, Navigation and Control of Unmanned Airships under Time-Varying Wind for Extended Surveillance

Abstract

This paper deals with the control of lighter-than-air vehicles, more specifically the design of an integrated guidance, navigation and control (GNC) scheme that is capable of navigating an airship through a series of constant-altitude, planar waypoints. Two guidance schemes are introduced, a track-specific guidance law and a proportional navigation guidance law, that provide the required signals to the corresponding controllers based on the airship position relative to a target waypoint. A novel implementation of the extended Kalman filter, namely the scheduled extended Kalman filter, estimates the required states and wind speed to enhance the performance of the track-specific guidance law in the presence of time-varying wind. The performance of the GNC system is tested using a high fidelity nonlinear dynamic simulation for a variety of flying conditions. Representative results illustrate the performance of the integrated system for chosen flight conditions.

An Improved Extended Kalman Filter for Radar Tracking of Satellite Trajectories

“…State estimation of an artificial satellite requires measurements that provide information about the satellite's position and velocity. This paper considers discrete measurements, given by Equation (6).…”

“…The results are represented in Figure 5. The radar measurements of this orbit are given by Equation (6), and the error standard deviations were considered to be as follows:…”

“…Nonlinear state estimation is a desirable and required tool in several engineering applications, especially in aerospace, where it is crucial for tasks such as surveillance, guidance, navigation, attitude control, obstacle avoidance and target tracking [1][2][3][4][5][6]. The problem consists of estimating the state vector (which contains all relevant information to describe the system of the moving target) based on noisy measurements, imperfect models, inaccurate data acquisition systems and environmental perturbations that are unwanted and, in most cases, also unknown [7].…”

Investigation and realisation of integrated navigation system using optimal pseudo sensor enhancement method

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