This work deals with the optimization of flight-test maneuvers for aerodynamic parameter estimation considering that the measurements are contaminated with colored residuals. The colored residuals consideration is important to give a direct and realistic assessment of the parameter estimation uncertainty levels before flight testing. The design technique is based on the optimization of the flight-test data information content and the Cramer-Rao lower bound. The discrete autocorrelation matrix of the measurement residuals is also used to compose the optimization criterion, thereby explicitly considering colored residuals. To validate the proposed technique, a flight-test campaign of the CEA-205 CB-9 Curumim was performed and its results discussed. The advantages of the proposed maneuver optimization technique are presented, stressing the ease of implementation of the signals and the strong improvement in the estimation procedures made possible with the application of the optimized maneuver signals. Nomenclature a z = vertical acceleration, g Cov() = covariance operator D = dispersion matrix Efg = mathematical expectation operator g = acceleration of gravity, m=s 2 J = cost function operator M = Fisher's information matrix N = vector length p† = probability density function operator q = pitch rate, rad=s R = measurement noise covariance matrix S = output sensitivity matrix with respect to system parameters t = time tr() = matrix trace operator u = input vector V tas = true airspeed, m=s = measurement noise vector x = state vector y = output vector = angle of attack, rad e = elevator deflection, rad = system parameter vector Subscript m = measurement variable Superscripts T = transpose 1 = matrix inversion = parameter estimate error

This work describes the application of the output-error method using the Levenberg-Marquardt optimization algorithm to the Flight Path Reconstruction (FPR) problem, which constitutes an important preliminary step towards the aircraft parameter identification. This method is also applied to obtain the aerodynamic and control derivatives of a regional jet aircraft from flight test data with measurement noise and bias. Experimental results are reported, employing a real jet aircraft, with flight test data acquired by smart probes, inertial sensors (gyrometers and accelerometers) and Global Positioning Systems (GPS) receivers.

In this work, two optimization algorithms are investigated to accomplish the parameter identification of the longitudinal motion of a real aircraft by using the output error method. The first algorithm is the nature-inspired algorithm named the life cycle model, which is a composed strategy based on other heuristics such as genetic algorithms and particle swarm optimization. The second one is the gradient-based technique named Levenberg-Marquardt algorithm, which is a variant of the Gauss-Newton method. Flight test data, performed with a training jet aircraft (Xavante AT-26), were used to feed the output error method. In this context, both optimization algorithms were tested, in solo performance and in a cascade-type approach. Results are reported, aiming to illustrate the success of using the proposed methodology.

This work deals with the optimization of flight-test maneuvers for aerodynamic parameter estimation considering that the measurements are contaminated with colored residuals. The colored residuals consideration is important to give a direct and realistic assessment of the parameter estimation uncertainty levels before flight testing. The design technique is based on the optimization of the flight-test data information content and the Cramer-Rao lower bound. The discrete autocorrelation matrix of the measurement residuals is also used to compose the optimization criterion, thereby explicitly considering colored residuals. To validate the proposed technique, a flight-test campaign of the CEA-205 CB-9 Curumim was performed and its results discussed. The advantages of the proposed maneuver optimization technique are presented, stressing the ease of implementation of the signals and the strong improvement in the estimation procedures made possible with the application of the optimized maneuver signals.