Tiperon

A control surface for an air vehicle (e.g., an aircraft, rocket, or missile) is useful for flight control at both subsonic and supersonic speeds. The control surface defines the outboardmost tip of a flight structure (e.g., a wing, tail or other stabilizer) of the air vehicle. Hence, the control surface is referred to as a `tiperon`. The tiperon has an approximately L-shaped configuration, and can be rotated relative to a fixed portion of the flight structure about a control axis. The respective surface areas of the tiperon sections forward and aft of the control axis are proportioned to place the subsonic center of pressure aft of the control axis to enhance aircraft control, and preferably also forward of the centroid of tiperon surface area. Also, the control surface sections forward and aft of the control axis are preferably mass-balanced, or at least nearly so, to enhance aircraft control at supersonic speeds. Either of the tiperon sections forward and aft of the control axis can be tapered to reduce the dependence of the moment exerted by air flow about the control axis, upon the tiperon's angle-of-attack. The tiperon also has enough surface area to control the air vehicle, even at low airspeeds. The invention is also directed to air vehicles incorporating one or more such control surfaces

Hardware Neural Network Implementation Of Tracking System

: A neural network (NN) filter/target-tracking system has been developed as reported in [6]. The design accepts and inputs signal data to a noise/target classifier which uses spectral estimation techniques to distinguish noise from real targets. In that design, the NN is used to calculate the coefficients of an auto regressive linear predictive filter. The current evolution of that design invokes the use of Lagrange Multiplier methods to incorporate known characteristics of the noise vs. signal. A (linear) Hopfield NN is used to perform the constrained optimization to solve for the filter coefficients. This algorithm has been demonstrated on real stochastic data. The filter resulting from this process succeeds in reducing the noise, whose structure was learned by the NN. Not only did this approach reduce structured noise without target attenuation or the addition of a `ghost' signal, but it also lowered the base level of the resultant signal significantly. The overall concept has been ..