AbstractThis paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.
Adaptive filter solution for processing lidar returns: optical parameter estimation
