Stability analysis of digital linear flight controllers subject to electromagnetic disturbances

High intensity electromagnetic radiation has been demonstrated to be a source of computer upsets in commercially available digital flight control systems. Such upsets can degrade the quality of the control signal ranging from a perturbation error over a few sample periods to a permanent error mode or computer failure. Under these conditions, the primary concern of the control engineer is to insure that the closed-loop system remains stable. A stochastic disturbance model and a set of associated stability assessment tools are introduced for determining stability robustness of a nominal closed-loop system subject to electromagnetic disturbances. The focus is primarily on night control applications, but the methodology is suitable for any application where highly reliable digital control is needed. The technique is demonstrated on a simple test example and on a stabilizing controller for the longitudinal dynamics of the AFTI/F-16 aircraft.     

Determination of crustal deformations based on GPS observing-session duration in Marmara region,Turkey

This paper reports on the estimation of changes of a GPS-based crustal deformation field as function of the observing session duration. This investigation was carried out on the Marmara Continuous GPS Network (Marmara region) by using GPS data collected on a set of 10 stations during the period 2002.50–2005.47 The GPS observations were processed in the ITRF 2005 reference frame using Bernese v5.0 software.     

Cumulant-based blind optimum beamforming

Sensor response, location uncertainty, and use of sample statistics can severely degrade the performance of optimum beamformers. We propose blind estimation of the source steering vector in the presence of multiple, directional, correlated or coherent Gaussian interferers via higher order statistics. In this way, we employ the statistical characteristics of the desired signal to make the necessary discrimination, without any a-priori knowledge of array manifold and direction-of-arrival (DOA) information about the desired signal. We then improve our method to utilize the data in a more efficient manner. In any application, only sample statistics are available, so we propose a robust beamforming approach that employs the steering vector estimate obtained by cumulant-based signal processing. We further propose a method that employs both covariance and cumulant information to combat finite sample effects. We analyze the effects of multipath propagation on the reception of the desired signal. We show that even in the presence of coherence, cumulant-based beamformer still behaves as the optimum beamformer that maximizes the signal-to-interference-plus-noise ratio (SINR). Finally, we propose an adaptive version of our algorithm simulations demonstrate the excellent performance of our approach in a wide variety of situations