Observability analysis of a bioinspired flexible flapping wing system provides a measure of how well the states of flexible flapping wing micro-aerial vehicles can be estimated from real-time measurements during high-speed flight. However, the traditional observability analysis approaches have trouble in terms of lack of quantitative analysis index, high computational complexity, low accuracy, and unavailability in stochastic systems with memory, including bioinspired flexible flapping wing systems. Therefore, a novel derivative-free observability analysis method is proposed here based on the generalized polynomial chaos expansion. By formulating a surrogate model to represent the relationship between the cumulative measurement and the random initial state, the observability coefficient matrix is calculated and the observability rank condition is stated. Consequently, several observability indices are proposed to quantity the observability of the system. Altogether, the proposed method avoids the disadvantages of the traditional approaches, especially in assessing the observability degree of each state and the effect of stochastic noise on observability. The validation of the proposed method is first provided by demonstrating the equivalence between the traditional and proposed methods and subsequently by comparing the observability of the Lorenz system calculated via three different approaches. Finally, the proposed method is applied on a bioinspired flexible wing system to optimize the placement of sensors, which is consistent with the natural configuration of campaniform sensilla on the wing of the hawkmoth.

The radome refraction problem gives rise to guidance performance deterioration for homing missiles. Aiming to eliminate the effect of radome refraction on the radar seeker, a novel method is proposed for correcting the radome-induced measurement error by using the estimated guidance information. A dynamic model for the estimation system is formulated and the guidance information is estimated online via a multiple-model filtering framework. A Gaussian process regression scheme is introduced to reconstruct the mapping model with respect to the radome error and look angle. Furthermore, an analytical expression for radome slope estimation is derived by calculating the derivative of the surrogate function, represented with Gaussian process models. The contaminated measurement is corrected based on the estimated guidance information and radome slope. Extensive simulation results illustrate that the proposed method is able to estimate the radome slope accurately and improve the guidance accuracy effectively.