A review of air–to–ground weapon aiming is given, with emphasis placed on the use of the head–up display (HUD), the main cockpit instrument used for accurate weapon aiming over the last 35 years. Nevertheless, the HUD is only of use for the aiming of forward–firing weapons. More advanced weapons have an off–axis capability and their aiming is greatly facilitated by the use of a helmet-mounted sight (HMS) or helmet–mounted display (HMD). The surface–to–air threat and the rules of engagement, particularly in operations other than war, place high demands on the aircrew and the weapon aiming system, both to stand off from the target and to have a high degree of confidence that it is the target. The requirement to perform an accurate in–flight transfer alignment of the weapon places further demands upon the aircrew. Timely and accurate target data, digitally received, plus an on–board targeting system which can automatically search for and recognise a target, are of great utility in the final stages prior to weapon release. The Defence Evaluation and Research Agency is performing research in these areas.

Benchmarks are defined, against which the performance of a given engine may be judged, with general indications for engine selection and aspects of design through which improvements may be sought. These are derived from published compilations of engine performance data obtained from suppliers around the world, which, if regularly refined and updated, will allow progress in small engine design to be systematically monitored and encouraged.

In this paper an algorithm for the trajectory tracking of a three-dimensional trajectory, assigned as a function of time, is presented. The proposed control system is suitable for application on unmanned aerial vehicles (UAVs) or for aircraft that require accurate path tracking, as in the case of rotorcraft in nap-of-the-earth (NOE) flight conditions. The control system logic features (i) an external loop based on a simple guidance scheme and a two-time-scale inverse simulation algorithm, and (ii) an inner loop, based on a linear-quadratic (LQ) full-state-feedback controller. In this way the control action is split into two contributions, i.e. a feedforward command, in order to follow the trajectory generated by the guidance scheme, and a feedback increment, for compensating external disturbances and model uncertainties. A rotorcraft model is used to demonstrate the algorithm capability in a NOE–like flight task. System robustness is analysed and control system performance are discussed in terms of the error between vehicle state and desired trajectory at a given time. Simulation of a representative manoeuvre shows that the feedforward estimate of the control action is accurate and only minor compensation is required from the LQ tracker. The algorithm is suitable for a number of applications, as (i) no simplifying assumptions are postulated for the model, (ii) there are no restrictions on the flight condition, and (iii) the computational time should allow for real–time implementation.

A computational investigation of the subsonic and transonic turbulent open flow over cavities was conducted. Simulations of these oscillatory flows were generated through time-accurate solutions of the Reynolds-averaged form of the Navier-Stokes equations. The effect of turbulence was included through the k–ω model. The results presented include calculations of the acoustic pressure distributions along the cavity floor, which compare well with experiment. The results are then used to describe the behaviour of the flow.

A flush air data system uses surface pressure measurements to obtain speed and aerodynamic orientation of a flight vehicle. This paper investigates the use of a neural network to calibrate the air data system on a low-observable aircraft forebody with 22 pressure tappings. Wind tunnel data were obtained for between 0 and 25° angle of attack, 0 to 10° sideslip for speeds of 32, 37 and 45ms-1. Experimental data were used to train multilayer perceptron neural networks. A calibration accuracy of 0·322ms-1, 0·811° and 0·552° for speed, alpha and beta respectively was achieved just using the first five tappings on the nose cone. Increasing the number of tappings used as inputs to the neural network reduces the calibration error. A neural network with 22 inputs gives a best accuracy of 0·095ms-1, 0·15° and 0·085° for speed, α and β respectively. Trained networks show poor robustness to single sensor failures. Robustness is improved by preprocessing input data with an autoassociative network or by introducing sensor redundancy.

The practice of using mathematical models to simulate pilot behaviour in one–axis stabilisation tasks is a well–known conventional simulation problem. In this paper an accepted mathematical model of a pilot is used as the controller of a rudimentary helicopter model. Flight test manoeuvre data from inverse simulation runs is used to provide time–histories which represent input forcing functions to the pilot–helicopter system, and a constrained optimisation routine is utilised to obtain values for the pilot gain and lead/lag equalisation parameters. It will be shown that as the theoretical pilot is required to ‘fly’ different manoeuvres, or indeed if the level of manoeuvre aggression is varied, the ‘pilot’ adjusts these parameters to perform a tracking task in compensatory control. The paper considers initially the pilot and helicopter models and subsequently analyses the whole system, illustrating how the pilot model changes with different situations.