No abstract

Contrast to the 6-DOF nonlinear dynamic modeling of nonlinear tracking problem, 3-DOF point-mass modeling of flight mechanics is efficient and adequate for applying the trajectory optimization problem. There exist limitations to apply an optimal trajectory from point-mass modeling as a reference trajectory directly to conduct the nonlinear tracking control. In this paper, new matching trajectory optimization scheme is proposed to compensate those differences of mismatching. To verify performance of proposed method, full ascent three-dimensional flight trajectories are obtained by reflecting the real constraints of flight conditions and airship performance with and without jet stream condition. Then, they are compared with the optimal trajectories obtained from conventional method.

This paper presents the procedures used for estimating the stability and control derivatives of a general aviation canard aircraft from flight data. The maximum likelihood estimation method which accounts for both process and measurement noise was used for the flight data analysis of a four seat canard aircraft, the Firefly. Without relying on the parameter estimation method, several aerodynamic derivatives were obtained by analyzing the steady state flight data. A wind tunnel test, a flight test of a 1/4 scaled remotely controlled model aircraft, and the prediction of aerodynamic coefficients using the USAF Stability and Control Digital Data Compendium (DATCOM), Advanced Aircraft Analysis (AAA), and Computer Fluid Dynamics (CFD) were performed during the development phase of the Firefly and the results were compared with flight determined derivatives of a full scaled flight prototype. A correlation between the results from each method could be used for the design of the canard aircraft as well as for building the aerodynamic database.

In this study, we develop a coarse-to-fine particle filter algorithm for track-before-detect in order to track a subpixelsized, low signal-to-noise ratio target in sensor data. The proposed algorithm enhances tracking performance in the presence of target motion uncertainty and it also maintains the computational load without increasing the number of particles. This coarse-to-fine particle filter, which is newly applied to track-before-detect, has two recursive stages: a coarse stage for extensive searches of the target's state space and a fine stage that narrows down the tracking results. During the coarse stage, particles are propagated with uniformly distributed noise to compensate for highly nonlinear target motion. The fine stage disturbs the particles filtered from the coarse stage using Gaussian distributed noise. Monte Carlo simulation results using artificial image sequences indicate improved performance with the proposed algorithm when uncertain large frame-to-frame pixel differences are caused by nonlinear target motions such as jittering effects. The algorithm is also applied to the real camera image frames to verify its detecting performance.

Vision-based collision avoidance system for air traffic management requires a excellent multiple target detection algorithm under low signal-to-noise ratio (SNR) levels. The track-before-detect (TBD) approaches have significant applications such as detection of small and dim targets from an image sequence. In this paper, two detection algorithms with the TBD approaches are proposed to satisfy the multiple target detection requirements. The first algorithm, based on a dynamic programming approach, is designed to classify multiple targets by using a k-means clustering algorithm. In the second approach, a hidden Markov model (HMM) is slightly modified for detecting multiple targets sequentially. Both of the proposed approaches are used in numerical simulations with variations in target appearance properties to provide satisfactory performance as multiple target detection methods.