Variational system identification is a new formulation of maximum likelihood for estimation of parameters of dynamical systems subject to process and measurement noise, such as aircraft flying in turbulence. This formulation is an alternative to the filter-error method that circumvents the solution of a Riccati equation and does not have problems with unstable predictors. In this paper, variational system identification is demonstrated for estimating aircraft parameters from real flight-test data. The results show that, in real applications of practical interest, it has better convergence properties than the filter-error method, reaching the optimum even when null initial guesses are used for all parameters and decision variables. This paper also presents the theory behind the method and practical recommendations for its use.

翻译：变分系统辨识是一种针对受过程噪声与测量噪声影响的动态系统（如湍流中飞行的飞行器）参数估计的最大似然估计新框架。该框架作为滤波误差法的替代方案，规避了Riccati方程的求解，且不存在不稳定预测器的问题。本文通过真实飞行试验数据对飞行器参数进行估计，验证了变分系统辨识方法的有效性。结果表明，在实际应用中，该方法较滤波误差法具有更优的收敛特性，即使所有参数和决策变量均采用零初始猜测值，仍能收敛至最优解。本文同时阐述了该方法的理论基础，并提供了实际应用建议。