Experiments and numerical analyses were conducted to investigate the reflection and shielding effects of jet noise by a horizontal tail and an aft-mounted engine. An acoustic test using a cold jet at a Mach number of 0.98 was carried out with a 28.8-mm-diameter exhaust nozzle and a T-tail model. The effects of the position and angle of the horizontal tail were investigated using the boundary element method and a fitted jet noise source model. The experimental results showed that the sound pressure level increased by 1.5 dB in the downward direction of the aircraft at a Strouhal number of 0.94. The numerical analysis results showed that the reflection effect was highly dependent on the relative position and size of the horizontal tail, the location of the source, and the angle of the horizontal tail. In addition, the effect of the reflected direction was ascertained to find an appropriate horizontal tail and engine arrangement that reduces noise. It was found that the greatest influence in terms of the effective perceived noise level occurred when the reflected direction was 60° from the jet axis.

Enhancing the observability of an integrated navigation system (i.e., triaxial-magnetometer-aided global positioning system (GPS) and inertial navigation system (INS)) is analyzed utilizing the Earth-centered-Earth-fixed (ECEF) coordinate system. The error states of the extended Kalman filter (EKF)-based GPS/INS integration algorithm are not fully observable given the position and velocity measurements of a single-antenna GPS receiver. Although the manner of maneuvering a vehicle can improve observability, full observability is not guaranteed. Measurements of a triaxial magnetometer provide attitude information and can enhance observability of the GPS/INS system. In this study, enhancing observability through aid of a triaxial magnetometer is investigated applying an analytic approach. The results of the analysis show that using a triaxial magnetometer allows the error states to be fully observable when the vehicle performs maneuvers. In addition, only one unobservable mode exists, even if the vehicle is in a static or non-accelerating condition.

A new method for dynamic sampling of Kriging surrogate models for uncertainty quantification is developed and presented. The criterion for the dynamic adaptive sampling proposed is based on combining the expected uncertainty of the fit and the gradient information resulting from the Kriging predictors, and an error-estimate term (based on the difference in the Kriging predictors with different correlation length scales). The Kriging-based dynamic adaptive sampling method proposed is tested on two-dimensional analytic functions with smoothly and steeply varied responses in the quantities of interest under normal uncertainty distributions. Compared with a classical polynomial chaos expansion method based on the Gauss quadrature rule and a dynamic adaptive sampling method based only on the uncertainty of the Kriging predictor fit, this new method shows superior performance for estimating the statistics of the quantity of interest in terms of both accuracy and robustness, and regardless of either the choice of the initial set of samples or the smoothness of the stochastic space.

We propose and investigate two approaches to identify load distributions on a flat panel by using strain measurement values. One approach is an inverse analysis that utilizes the inverse matrix of the load and strain relationship, and the other is a neural network approach that trains a neural network using strains as input and loads as output. For both approaches, we propose a method using a pressure discretization map to represent the load distributions as a set of discrete pressure values. This method makes load identification applicable to load distributions with arbitrary profiles. In order to examine and verify the performance, we conducted numerical simulations and an experiment. Numerical simulation results verified both approaches; however, identification results using the inverse approach were unstable when the strain measurement error existed. On the other hand, the neural network approach showed high robustness to the strain errors by training neural networks with data including artificial strain errors. Based on the results, we discuss the applicability of the load identification approaches.

To deal with randomly delayed measurements and glint noise, a novel recursive filter referred as a randomly delayed genetic resampling particle filter (RD-GRPF) is proposed in this paper. By making use of Bernoulli random variables, the measurement model is modified to describe the random delay. Then, the weight update equation is reformulated based on this model. To avoid the particle degeneration and sample impoverishment that always arise in the application of a particle filter, the genetic resampling method is utilized to resample the particles. Then, the RD-GRPF is obtained. Therefore, the filter proposed not only copes with randomly delayed measurements, it also keeps the advantage of the standard particle filter, which ensures good performance in the case of non-Gaussian noise. In addition, the RD-GRPF proposed is applied to line-of-sight (LOS) rate estimation, the model for which is also presented in this paper. A simulation was conducted and the results demonstrate the superiority of the RD-GRPF.