MODIFIED RECURSIVE MAXIMUM-LIKELIHOOD ADAPTIVE FILTER FOR NONLINEAR AIRCRAFT FLIGHTPATH RECONSTRUCTION

Nonlinear aircraft flight-path reconstruction is basically a state est imation problem that can be solved with adaptive filtering techniques, as it involves the estimation of flight trajectories and unknown para meters such as biases, scale factors, and noise statistical uncertaint ies of flight instrumentation systems. Among many algorithms, the recu rsive maximum likelihood (RML) method is a popular scheme in adaptive filtering for nonlinear state-parameter estimation problems. However, the RML algorithm is sensitive to initialization errors of system para meters. Divergence may occur at large values of these errors. The obje ctive of the present study is to develop a modified recursive maximum likelihood (MRML) adaptive filter that is less sensitive to the effect s of initialization errors. The new algorithm revises the conventional RML adaptive biter by including the effect of the parameter estimator in the prediction error vector computation. Numerical results are pre sented for a nonlinear aircraft model. System states and a variety of parameters including measurement noise standard deviations were estima ted with the conventional RML adaptive filter and compared with corres ponding estimates of the new MRML adaptive filter. Numerical simulatio ns were carried out with different a priori estimates of parameters an d system state vector elements. The results indicate that the MRML ada ptive filter, as developed here, produces estimates that are both more accurate and less sensitive to parameter initialization errors than t hose obtained with the conventional RML adaptive filter.