In this paper, for a class of nonlinear systems, a robust augmented extended Kalman filter is proposed based on descriptor systems combined with the improve whale optimization algorithm to search the system noise optimal solution, so that the accurate estimation of concurrent actuator and sensor faults for the nonlinear system is implemented. Firstly, the concurrent faults are regarded as the state variable of the nonlinear system, a descriptor systems is established, and the fault estimation of the nonlinear system is transformed into the state estimation of the nonlinear descriptor systems. Then, a robust upper bound is proposed to decrease the influence of linearization error on estimation accuracy. Furthermore, the noise is optimized by the improve whale optimization algorithm to optimize the robust augmented extended Kalman filter. Finally, the longitudinal motion simulation model of F-16 aircraft is given, the algorithm designed in this paper is used to compare with adaptive unscented Kalman filter and robust augmented extended Kalman filter based on whale optimization algorithm. The simulation results show that compared with the other two algorithms, the root mean square error of fault estimation of the algorithm designed in this paper is reduced by about 50%, which verifies its superiority.